diff --git a/workflow/.old/templates/qhtcp/Exp2/.DS_Store b/workflow/.old/templates/qhtcp/Exp2/.DS_Store
new file mode 100644
index 00000000..cae6fe4d
Binary files /dev/null and b/workflow/.old/templates/qhtcp/Exp2/.DS_Store differ
diff --git a/workflow/.old/templates/qhtcp/Exp2/ExpFrontend.m b/workflow/.old/templates/qhtcp/Exp2/ExpFrontend.m
new file mode 100644
index 00000000..25a6399b
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp2/ExpFrontend.m
@@ -0,0 +1,214 @@
+%Frontend240417.m
+%augmented ExpFrontend to provide a quick text archive and a robust
+%database ammenable archive
+%FrontEnd utility to copy source result sheet into Exp_ folders of
+%StudiesQHTCP/StudyName/Exp1(2,3,4). This allow the automation of path
+%capture to the StudiesDataArchieve.txt study log.
+%Select, copy and Capture Study Exp_ details to study log
+
+%Exp meta data collection 
+W=pwd;
+%Load results file meta data  into workspace
+try 
+ExpLabel= strcat('Exp',W(end))
+questdlg('\fontsize{20} Select the !!Results File','File Selection','OK', struct('Default','OK','Interpreter','tex'));
+[resFile,resPath]= uigetfile('*.txt')
+copyfile((fullfile(resPath,resFile)),fullfile(W))
+resDate= char(regexp(resFile, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)','match'))
+cd ..
+Wstudy= pwd
+studyDate= datetime('now');
+S.sDate(1) = {studyDate};
+if ispc
+    lastSep=max(strfind(Wstudy,'\'))
+    studyName= Wstudy((lastSep+1):end)
+else
+    lastSep=max(strfind(Wstudy,'/'))
+    studyName= Wstudy((lastSep+1):end)
+end
+
+S.sDate(1)= {studyDate};
+S.sName(1)= {studyName}
+S.sPath(1)= {Wstudy}
+S.ELabel(1)= {ExpLabel}
+S.EresDate(1)= {resDate}
+S.EresFile(1)= {resFile}
+S.EresPath(1)= {resPath}
+   
+cd ..
+
+fid = fopen('StudiesDataArchive.txt','a');  
+fprintf(fid,'StudyDate\tStudyName\tStudyPath\tExpNum\tExpDate\tExpPath\tResultFile\n');
+fprintf(fid, '%s\t %s\t %s\t %s\t %s\t %s\t %s \n',S.sDate{1},S.sName{1},S.sPath{1},S.ELabel{1},S.EresDate{1},S.EresPath{1},S.EresFile{1});
+fclose(fid);
+fclose('all');
+
+catch
+    cd(W)
+    disp('Error: Unable to Execute ExpFrontend.m')
+end
+
+cd(W)
+pwd
+
+
+
+%*************************************************************************************
+%*************************************************************************************
+%#####################################################################################
+%Improved storage ammenable for database use
+%FrontEnd utility to copy source result sheet into Exp_ folders of
+%StudiesQHTCP/StudyName/Exp1(2,3,4). This allow the automation of path
+%capture to the StudiesDataArchieve.txt study log.
+%Select, copy and Capture Study Exp_ details to study log
+studyDateNow= studyDate  %preserve the 'Now' date 
+nowNumFNm= strcat((int2str(now)),'.mat') % from previous section incase the /StudyName doesn't have a standard date 
+%capture the /StudiesQHTCP directory for storing log data
+cd ../..
+logPath= pwd  %is /.../Exp_
+cd(W)
+%Try to load an existing data set from previous Frontend calls
+try  %end ~ln121
+  load(fullfile(logPath,'.studyLog.mat')) %load(fullfile('../../','studyLog.mat'))
+catch  %If no studyLog.mat found [Initial First Entry]
+    ExpLabel= strcat('Exp',W(end))
+    resDate= char(regexp(resFile, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)','match'))
+    cd .. %move up to current study folder from ../Exp_ folder; 
+    %Study meta data collection
+    Wstudy= pwd %Capture the specific current Study directory
+    try %try to extract study date from folder name
+     studyDate= char(regexp(Wstudy, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)| \d\d\d\d\d\d)','match'))
+     S.sDate(1)= {studyDate};
+    catch %if unsuccessful, accept the current date and convert to a 6 char string
+     studyDate= yyyymmdd(studyDateNow);
+     studyDate= (int2str(studyDate))
+     studyDate= studyDate(3:8)
+    end
+   %Capture StudyName
+   if ispc
+    lastSep=max(strfind(Wstudy,'\'))
+    studyName= Wstudy((lastSep+1):end)
+   else
+    lastSep=max(strfind(Wstudy,'/'))
+    studyName= Wstudy((lastSep+1):end)
+    
+   end
+  %Put current First data entry into Structure of Save to StudyLog.mat file
+   S.sDate(1)= {studyDate};
+   S.sName(1)= {studyName}
+   S.sPath(1)= {Wstudy}
+   S.ELabel(1)= {ExpLabel}
+   S.EresDate(1)= {resDate}
+   S.EresFile(1)= {resFile}
+   S.EresPath(1)= {resPath}
+   
+ cd(W)
+
+logFiletxt= fullfile(logPath,'StudiesDataArchive4DB.txt') %relative .txt path 
+%Initialize the StudiesDataArchive.txt file with the First row of meta data
+
+%Print to a .txt spreadsheet the first set of data in the logFiletxt file
+fid = fopen(logFiletxt,'w');
+fprintf(fid,'StudyDate\tStudyName\tStudyPath\tExpNum\tExpDate\tExpPath\tResultFile\n');
+fprintf(fid, '%s\t %s\t %s\t %s\t %s\t %s\t %s \n',studyDate,studyName,Wstudy,ExpLabel,resDate,resPath,resFile);
+fclose(fid);
+%Save the first entry meta data into a permanent .mat files for future recall
+save(fullfile(logPath,'studyLog4DB.mat'), 'S')
+save(fullfile(logPath,'.studyLog.mat'), 'S')
+save(fullfile(logPath,'.nowNumFNm.mat'), 'S')
+
+
+
+end  %end for try  for the First entry only
+%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+%++++++++END of FIRST ENTRY startup section++++++++++++++++++++++++++++++++
+%++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+
+%Begin routine for all Entries After the Initial data entry**************** 
+ExpLabel= strcat('Exp',W(end))
+resDate= char(regexp(resFile, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)','match')) %Capture date from !!Results file
+
+cd .. %Move to the current study folder
+
+%Capture study meta dat
+Wstudy= pwd % Capture the path to the current study
+
+  %Attempt to extract study date from folder name and convert to a
+  %searchable numeric date
+   studyDate= char(regexp(Wstudy, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)| \d\d\d\d\d\d)','match'))
+      if ~isempty(studyDate)  %If no date in folder name use current date and convert to 6char string
+       S.sDate(1)= {studyDate};
+       if isequal((str2num(studyDate(3))),'_') %Remove '_' so that date is a number for easy DB search
+           studyDate= strcat(studyDate(1:2),studyDate(4:6))
+       end
+      else
+       studyDate= yyyymmdd(studyDateNow);
+       studyDate= (int2str(studyDate)) 
+       studyDate= studyDate(3:8) 
+     end
+    
+if ispc
+    lastSep=max(strfind(Wstudy,'\'))
+    studyName= Wstudy((lastSep+1):end)
+else
+    lastSep=max(strfind(Wstudy,'/'))
+    studyName= Wstudy((lastSep+1):end)
+end
+
+%Put meta data into a structure for storage (studyLog.mat) and future
+%compare operations
+lastRow= length(S.sPath)
+
+%Compare these just obtained paths with those stored in the studyLog.mat
+%file and add a new row if the study or results sheet meta data is new or different
+cd ..  %Move the the /QHTPCstudies folder
+logtxt= 'StudiesDataArchive4DB.txt'
+logFiletxt= fullfile(logPath,logtxt) %
+try
+  matched=0;
+ for n= 1:(lastRow)
+    if strcmp(Wstudy,S.sPath(n)) && strcmp(resPath, S.EresPath(n)) && strcmp(ExpLabel, S.ELabel(n))
+        matched= 1;
+        break
+    end
+ end
+      
+    
+%Update the StudiesDataArchive.txt file with a new row of meta data
+if matched== 0 %if no match for this entry then add a line to the text archive file
+     fid = fopen(logFiletxt,'w');  
+      S.sDate(lastRow+1)= {studyDate};
+      S.sName(lastRow+1)= {studyName};
+      S.sPath(lastRow+1)= {Wstudy};
+      S.ELabel(lastRow+1)= {ExpLabel};
+      S.EresDate(lastRow+1)= {resDate};
+      S.EresFile(lastRow+1)= {resFile};
+      S.EresPath(lastRow+1)= {resPath};
+  for n= 1:(lastRow +1)
+   %Print to a .txt spreadsheet and Save update to .mat files
+    if n==1
+     fprintf(fid,'StudyDate\tStudyName\tStudyPath\tExpNum\tExpDate\tExpPath\tResultFile\n');
+    end
+    fprintf(fid, '%s\t %s\t %s\t %s\t %s\t %s\t %s \n',S.sDate{n},S.sName{n},S.sPath{n},S.ELabel{n},S.EresDate{n},S.EresPath{n},S.EresFile{n});
+  end
+
+  fclose(fid); %close DBase amenable study log file after data update
+ 
+ save((fullfile(logPath,'studyLog4DB.mat')), 'S') % path for  studyLog.mat
+ save((fullfile(logPath,'.studyLog.mat')), 'S')  %path backup studyLogBU.mat
+ save((fullfile(logPath,strcat('.',nowNumFNm))), 'S') 
+end 
+fclose('all');
+
+catch
+    cd(W) %Return to the location of the frontend.m code (/studyName/Exp_)
+    fclose('all');
+    clear S %clear data structure variable 'S.'
+end
+
+cd(W) %Return to the location of the frontend.m code (/studyName/Exp_)
+clear all %clear workspace variables
+
+
diff --git a/workflow/.old/templates/qhtcp/Exp2/ZScores/.DS_Store b/workflow/.old/templates/qhtcp/Exp2/ZScores/.DS_Store
new file mode 100644
index 00000000..9f5f58c5
Binary files /dev/null and b/workflow/.old/templates/qhtcp/Exp2/ZScores/.DS_Store differ
diff --git a/workflow/.old/templates/qhtcp/Exp2/Z_InteractionTemplate.R b/workflow/.old/templates/qhtcp/Exp2/Z_InteractionTemplate.R
new file mode 100644
index 00000000..fe31ca14
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp2/Z_InteractionTemplate.R
@@ -0,0 +1,2730 @@
+#Based on InteractionTemplate.R which is based on Sean Santose's Interaction_V5 script.
+#Adapt SS For Structured Data storage but using command line scripts
+###Set up the required libraries, call required plot theme elements and set up the command line arguments
+library("ggplot2")
+library("plyr")
+library("extrafont")
+library("gridExtra")
+library("gplots")
+library("RColorBrewer")
+library("stringr")
+#library("gdata")
+library(plotly)
+library(htmlwidgets)
+
+Args <- commandArgs(TRUE)
+input_file <- Args[1] #"!!Results_17_0827_yor1null-rpl12anull misLabeledAsFrom MI 17_0919_yor1-curated.txt"  #Args[1]  #Arg 1 #"!!ResultsStd_JS 19_1224_HLEG_P53.txt" is the !!results ... .txt
+#Tool to find a file and copy it to desired location
+destDir= getwd()
+#srcFile= file.choose()
+#file.copy(srcFile, destDir)
+#input_file= tail(strsplit(srcFile,"[/]")[[1]],1)
+
+
+
+#Path to Output Directory
+#W=getwd() #R is F'd up, Can't use, Any legitamate platform could build out dirs from this
+outDir <- "ZScores/" #"Args[2] #paste0(W,"/ZScores/")
+subDir <- outDir     #Args[2]
+
+if (file.exists(subDir)){
+  outputpath <- subDir
+} else {
+  dir.create(file.path(subDir))
+}
+
+if (file.exists(paste(subDir,"QC/",sep=""))){
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+} else {
+  dir.create(file.path(paste(subDir,"QC/",sep="")))
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+}
+#define the output path (formerly the second argument from Rscript)
+outputpath <- outDir 
+
+#Set Args[2] the Background contamination noise filter as a function of standard deviation
+#std= as.numeric(Args[2])
+#Sean recommends 3 or 5 SD factor.
+#Capture Exp_ number,use it to Save Args[2]{std}to Labels field and then Write to Labels to studyInfo.txt for future reference
+Labels <- read.csv(file= "../Code/StudyInfo.csv",stringsAsFactors = FALSE)  #,sep= ",")
+print("Be sure to enter Background noise filter standard deviation i.e., 3 or 5 per Sean")
+
+#User prompt for std multiplier Value
+cat("Enter a Standard Deviation value to noise filter \n")
+inpChar<- readLines(file("stdin"), n = 1L)
+cat(paste("Standard Deviation Value is", inpChar, "\n"))
+inpNum= as.numeric(inpChar)
+#set std deviation multiplier default if no user entry
+if(!is.na(inpNum)){
+  std= inpNum
+}else{std= 3}
+
+
+expNumber<- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd()))
+Labels[expNumber,3]= as.numeric(std)
+Delta_Background_sdFactor <- std
+DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#Write Background SD value to studyInfo.txt file
+#write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+print('ln 50 write StudyInfo.csv ')
+#write.table(Labels,file=paste(outputpath,"StudyInfo.txt"),sep = "\t",row.names = FALSE)
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++BEGIN USER DATA SELECTION SECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+
+#read in the data
+X <- read.delim(input_file,skip=2,as.is=T,row.names=1,strip.white=TRUE)
+X <- X[!(X[[1]]%in%c("","Scan")),]
+#X <- X[!(X[[1]]%in%c(61:76)),]  #Remove dAmp plates which are Scans 61 thru 76
+
+#X <- X[which(X$Specifics == "WT"),]
+
+#X_length <- length(X[1,])
+#X_end <- length(X[1,]) - 2
+#X <- X[,c(1:42,X_end:X_length)]
+
+
+#use numeric data to perform operations
+X$Col <- as.numeric(X$Col)
+X$Row <- as.numeric(X$Row)
+X$l <- as.numeric(X$l)
+X$K <- as.numeric(X$K)
+X$r <- as.numeric(X$r)
+X$Scan <- as.numeric(X$Scan)
+X$AUC <- as.numeric(X$AUC)
+X$LstBackgrd <- as.numeric(X$LstBackgrd)
+X$X1stBackgrd <- as.numeric(X$X1stBackgrd)
+
+#Sometimes the an experimenter may have placed the non-varying drug in the 'Drug' col instead of the 'Modifier1' col
+#as was the case in Gemcitabin and Cytarabin experiments.
+#The following allows user to rename columns so as to get the appropriate
+#data where it needs to be for the script to run properly.
+#colnames(X)[7]  <- "Modifier1"
+#colnames(X)[8]  <- "Conc1"
+#colnames(X)[10]  <- "Drug"
+#colnames(X)[11]  <- "Conc"
+
+#set the OrfRep to YDL227C for the ref data
+X[X$ORF == "YDL227C",]$OrfRep <- "YDL227C"
+#Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. 
+#That is the first DM plates are removed from the data set with the following.
+#X <- X[X$Conc1 != "0ug/ml",] #This removes data with dox ==0 leaving gene expression on with four different concentrations of Gemcytabin
+X <- X[X$Drug != "BMH21",] #This removes data concerning BMH21 for this experiment
+
+#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. 
+#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used.
+#try(X <- X[X$ORF != "bad_spot",])
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=
+
+#get total number of drug concentrations
+Total_Conc_Nums <- length(unique(X$Conc))
+
+#function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
+numextract <- function(string){ 
+  str_extract(string, "\\-*\\d+\\.*\\d*")
+} 
+
+#generate a new column with the numeric drug concs
+X$Conc_Num <- as.numeric(numextract(X$Conc))
+#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
+X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1
+
+#Get the max factor for concentration
+MAX_CONC <- max(X$Conc_Num_Factor)
+#if treating numbers not as factors uncomment next line and comment out previous line
+#MAX_CONC <- max(X$Conc_Num)
+
+#remove wells with problems for making graphs and to not include in summary statistics
+X <- X[X$Gene != "BLANK",]
+X <- X[X$Gene != "Blank",] 
+X <- X[X$ORF != "Blank",]
+X <- X[X$Gene != "blank",]
+#X <- X[X$Gene != "HO",]
+Xbu= X
+#Inserted to use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script). This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
+#Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to write over the RF1&RF2 geneNames which caused a variance with his code results 
+#in the Z_lm_L,K,r&AUC output values. Values correlated well but were off by a multiplier factor.
+SGDgeneList= "../Code/SGD_features.tab"
+genes = data.frame(read.delim(file=SGDgeneList,quote="",header=FALSE,colClasses = c(rep("NULL",3), rep("character", 2), rep("NULL", 11))))
+for(i in 1:length(X[,14])){
+  ii= as.numeric(i)
+  line_num = match(X[ii,14],genes[,1],nomatch=1)
+  OrfRepColNum= as.numeric(match('OrfRep',names(X)))
+  if(X[ii,OrfRepColNum]!= "YDL227C"){
+     X[ii,15] = genes[line_num,2]
+  }
+  if((X[ii,15] == "")||(X[ii,15] == "OCT1")){
+    X[ii,15] = X[ii,OrfRepColNum]
+  }
+}
+Xblankreplace= X
+#X= Xbu #for restore testing restore X if geneName 'Match' routine needs changing
+
+#Remove dAmPs *******************************
+#jlh confirmed to leave dAmps in so comment out this section
+#DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
+#Damps <- read.delim(DAmPs_List,header=F)
+
+#X <- X[!(X$ORF %in% Damps$V1),]  #fix this to Damps[,1]
+#XafterDampsRM=X  #Backup for debugging especially when Rstudio goes crazy out of control
+# ***********
+
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++END USER DATA SELECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+print("ln137 End of User Section including blank gene writeOver")
+#++++Begin Graphics Boiler Plate Section+++++++++++++++++++++++++++++++++++++++
+#theme elements for plots
+theme_Publication <- function(base_size=14, base_family="sans") {
+  library(grid)
+  library(ggthemes)
+  (theme_foundation(base_size=base_size, base_family=base_family)
+    + theme(plot.title = element_text(face = "bold",
+                                      size = rel(1.2), hjust = 0.5),
+            text = element_text(),
+            panel.background = element_rect(colour = NA),
+            plot.background = element_rect(colour = NA),
+            panel.border = element_rect(colour = NA),
+            axis.title = element_text(face = "bold",size = rel(1)),
+            axis.title.y = element_text(angle=90,vjust =2),
+            axis.title.x = element_text(vjust = -0.2),
+            axis.text = element_text(), 
+            axis.line = element_line(colour="black"),
+            axis.ticks = element_line(),
+            panel.grid.major = element_line(colour="#f0f0f0"),
+            panel.grid.minor = element_blank(),
+            legend.key = element_rect(colour = NA),
+            legend.position = "bottom",
+            legend.direction = "horizontal",
+            legend.key.size= unit(0.2, "cm"),
+            legend.spacing = unit(0, "cm"),
+            legend.title = element_text(face="italic"),
+            plot.margin=unit(c(10,5,5,5),"mm"),
+            strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+            strip.text = element_text(face="bold")
+    ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  library(scales)
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
+  (theme_foundation(base_size=base_size, base_family=base_family)
+   + theme(plot.title = element_text(face = "bold",
+                                     size = rel(1.2), hjust = 0.5),
+           text = element_text(),
+           panel.background = element_rect(colour = NA),
+           plot.background = element_rect(colour = NA),
+           panel.border = element_rect(colour = NA),
+           axis.title = element_text(face = "bold",size = rel(1)),
+           axis.title.y = element_text(angle=90,vjust =2),
+           axis.title.x = element_text(vjust = -0.2),
+           axis.text = element_text(), 
+           axis.line = element_line(colour="black"),
+           axis.ticks = element_line(),
+           panel.grid.major = element_line(colour="#f0f0f0"),
+           panel.grid.minor = element_blank(),
+           legend.key = element_rect(colour = NA),
+           legend.position = "right",
+           legend.direction = "vertical",
+           legend.key.size= unit(0.5, "cm"),
+           legend.spacing = unit(0, "cm"),
+           legend.title = element_text(face="italic"),
+           plot.margin=unit(c(10,5,5,5),"mm"),
+           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+           strip.text = element_text(face="bold")
+   ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+#print timestamp for initial time the code starts
+timestamp()
+#+++++BEGIN QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+###Part 2 - Quality control
+#print quality control graphs for each dataset before removing data due to contamination
+#and before adjusting missing data to max theoretical values
+
+#plate analysis plot
+#plate analysis is a quality check to identify plate effects containing anomalies
+
+Plate_Analysis_L <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+
+
+Plate_Analysis_L_Box <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K_Box <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r_Box <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+#quality control - values with a high delta background likely have heavy contamination
+#check the frequency of these values
+#report the L and K values of these spots
+#report the number to be removed based on the Delta_Background_Tolerance
+X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd
+
+
+#raw l vs K before QC
+Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle("Raw L vs K before QC") +
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8)
+Raw_l_vs_K_beforeQC
+dev.off()
+pgg <- ggplotly(Raw_l_vs_K_beforeQC)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_beforeQC.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+
+#set delta background tolerance based on 3 sds from the mean delta background
+Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(DelBGFactr*sd(X$Delta_Backgrd))
+#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
+print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" "))
+
+Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+
+
+X_Delta_Backgrd_above_Tolerance <- X[X$Delta_Backgrd >= Delta_Background_Tolerance,]
+
+X_Delta_Backgrd_above_Tolerance_K_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$K,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_L_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$l,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_toRemove <- dim(X_Delta_Backgrd_above_Tolerance)[1]
+
+X_Delta_Backgrd_above_Tolerance_L_vs_K <- ggplot(X_Delta_Backgrd_above_Tolerance,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle(paste("Raw L vs K for strains above delta background threshold of",Delta_Background_Tolerance,"or above")) +
+  annotate("text",x=X_Delta_Backgrd_above_Tolerance_L_halfmedian,y=X_Delta_Backgrd_above_Tolerance_K_halfmedian,
+           label = paste("Strains above delta background tolerance = ",X_Delta_Backgrd_above_Tolerance_toRemove)) + 
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8)
+X_Delta_Backgrd_above_Tolerance_L_vs_K
+dev.off()
+pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+#frequency plot for all data vs. the delta_background
+DeltaBackground_Frequency_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_density() +
+  ggtitle("Density plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+#bar plot for all data vs. the delta_background
+DeltaBackground_Bar_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_bar() +
+  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+pdf(file = paste(outputpath_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8)
+print(DeltaBackground_Frequency_Plot)
+print(DeltaBackground_Bar_Plot)
+dev.off()
+
+
+#Need to identify missing data, and differentiate between this data and removed data so the removed data can get set to NA and the missing data can get set to max theoretical values
+#1 for missing data, 0 for non missing data
+#Use "NG" for NoGrowth rather than "missing"
+X$NG <- 0
+try(X[X$l == 0 & !is.na(X$l),]$NG <- 1)
+
+#1 for removed data, 0 non removed data
+#Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
+X$DB <- 0
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$DB <- 1)
+
+#replace the CPPs for l, r, AUC and K (must be last!) for removed data
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$l <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$r <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$AUC <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$K <- NA)
+
+
+Plate_Analysis_L_afterQC <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L
+Plate_Analysis_L_afterQC
+Plate_Analysis_K
+Plate_Analysis_K_afterQC
+Plate_Analysis_r
+Plate_Analysis_r_afterQC
+Plate_Analysis_AUC
+Plate_Analysis_AUC_afterQC
+Plate_Analysis_Delta_Backgrd
+Plate_Analysis_Delta_Backgrd_afterQC
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box
+Plate_Analysis_L_Box_afterQC
+Plate_Analysis_K_Box
+Plate_Analysis_K_Box_afterQC
+Plate_Analysis_r_Box
+Plate_Analysis_r_Box_afterQC
+Plate_Analysis_AUC_Box
+Plate_Analysis_AUC_Box_afterQC
+Plate_Analysis_Delta_Backgrd_Box
+Plate_Analysis_Delta_Backgrd_Box_afterQC
+dev.off()
+
+#remove the zero values and print plate analysis
+X_noZero <- X[which(X$l > 0),]
+Plate_Analysis_L_afterQC_Z <- ggplot(X_noZero,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC_Z <- ggplot(X_noZero,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC_Z <- ggplot(X_noZero,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC_Z <- ggplot(X_noZero,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC_Z <- ggplot(X_noZero,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L_afterQC_Z
+Plate_Analysis_K_afterQC_Z
+Plate_Analysis_r_afterQC_Z
+Plate_Analysis_AUC_afterQC_Z
+Plate_Analysis_Delta_Backgrd_afterQC_Z
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box_afterQC_Z
+Plate_Analysis_K_Box_afterQC_Z
+Plate_Analysis_r_Box_afterQC_Z
+Plate_Analysis_AUC_Box_afterQC_Z
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
+dev.off()
+
+#remove dataset with zeros removed
+rm(X_noZero)
+
+
+#X_test_missing_and_removed <- X[X$Removed == 1,]
+
+#calculate summary statistics for all strains, including both background and the deletions
+X_stats_ALL <- ddply(X, c("Conc_Num","Conc_Num_Factor"), summarise,
+                     N = (length(l)),
+                     mean_L = mean(l,na.rm=TRUE),
+                     median_L = median(l,na.rm=TRUE),
+                     max_L = max(l,na.rm=TRUE),
+                     min_L = min(l,na.rm=TRUE),
+                     sd_L = sd(l,na.rm=TRUE),
+                     se_L = sd_L / sqrt(N-1),
+                     mean_K = mean(K,na.rm=TRUE),
+                     median_K = median(K,na.rm=TRUE),
+                     max_K = max(K,na.rm=TRUE),
+                     min_K = min(K,na.rm=TRUE),
+                     sd_K = sd(K,na.rm=TRUE),
+                     se_K = sd_K / sqrt(N-1),
+                     mean_r = mean(r,na.rm=TRUE),
+                     median_r = median(r,na.rm=TRUE),
+                     max_r = max(r,na.rm=TRUE),
+                     min_r = min(r,na.rm=TRUE),
+                     sd_r = sd(r,na.rm=TRUE),
+                     se_r = sd_r / sqrt(N-1),
+                     mean_AUC = mean(AUC,na.rm=TRUE),
+                     median_AUC = median(AUC,na.rm=TRUE),
+                     max_AUC = max(AUC,na.rm=TRUE),
+                     min_AUC = min(AUC,na.rm=TRUE),
+                     sd_AUC = sd(AUC,na.rm=TRUE),
+                     se_AUC = sd_AUC / sqrt(N-1)
+)
+#print(X_stats_ALL_L)
+
+write.csv(X_stats_ALL,file=paste(outputpath,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE)
+#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+##### Part 3 - Generate summary statistics and calculate the max theoretical L value
+##### Calculate the Z score at each drug conc for each deletion strain
+
+
+#get the background strains - can be modified to take another argument but for most screens will just be YDL227C
+Background_Strains <- c("YDL227C")
+
+#first part of loop will go through for each background strain 
+#most cases there will only be one YDL227C
+for(s in Background_Strains){
+  X_Background <- X[X$OrfRep == s,]
+  
+  #if there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
+  #we may want to consider in some cases giving the max high value to L depending on the data type
+  if(table(X_Background$l)[1] == 0){
+    X_Background[X_Background$l == 0,]$l <- NA
+    X_Background[X_Background$K == 0,]$K <- NA
+    X_Background[X_Background$r == 0,]$r <- NA
+    X_Background[X_Background$AUC == 0,]$AUC <- NA
+  }
+  
+  X_Background <- X_Background[!is.na(X_Background$l),]
+  
+  #get summary stats for L, K, R, AUC
+  X_stats_BY_L <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(l)),
+                        mean = mean(l,na.rm=TRUE),
+                        median = median(l,na.rm=TRUE),
+                        max = max(l,na.rm=TRUE),
+                        min = min(l,na.rm=TRUE),
+                        sd = sd(l,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L)
+  X1_SD <- max(X_stats_BY_L$sd)
+  
+  X_stats_BY_K <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(K)),
+                        mean = mean(K,na.rm=TRUE),
+                        median = median(K,na.rm=TRUE),
+                        max = max(K,na.rm=TRUE),
+                        min = min(K,na.rm=TRUE),
+                        sd = sd(K,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_K <- max(X_stats_BY_K$sd)
+  
+  
+  X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = length(r),
+                        mean = mean(r,na.rm=TRUE),
+                        median = median(r,na.rm=TRUE),
+                        max = max(r,na.rm=TRUE),
+                        min = min(r,na.rm=TRUE),
+                        sd = sd(r,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_r <- max(X_stats_BY_r$sd)
+  
+  X_stats_BY_AUC <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                          N = length(AUC),
+                          mean = mean(AUC,na.rm=TRUE),
+                          median = median(AUC,na.rm=TRUE),
+                          max = max(AUC,na.rm=TRUE),
+                          min = min(AUC,na.rm=TRUE),
+                          sd = sd(AUC,na.rm=TRUE),
+                          se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
+  
+  X_stats_BY <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                      N = (length(l)),
+                      mean_L = mean(l,na.rm=TRUE),
+                      median_L = median(l,na.rm=TRUE),
+                      max_L = max(l,na.rm=TRUE),
+                      min_L = min(l,na.rm=TRUE),
+                      sd_L = sd(l,na.rm=TRUE),
+                      se_L = sd_L / sqrt(N-1),
+                      mean_K = mean(K,na.rm=TRUE),
+                      median_K = median(K,na.rm=TRUE),
+                      max_K = max(K,na.rm=TRUE),
+                      min_K = min(K,na.rm=TRUE),
+                      sd_K = sd(K,na.rm=TRUE),
+                      se_K = sd_K / sqrt(N-1),
+                      mean_r = mean(r,na.rm=TRUE),
+                      median_r = median(r,na.rm=TRUE),
+                      max_r = max(r,na.rm=TRUE),
+                      min_r = min(r,na.rm=TRUE),
+                      sd_r = sd(r,na.rm=TRUE),
+                      se_r = sd_r / sqrt(N-1),
+                      mean_AUC = mean(AUC,na.rm=TRUE),
+                      median_AUC = median(AUC,na.rm=TRUE),
+                      max_AUC = max(AUC,na.rm=TRUE),
+                      min_L = min(AUC,na.rm=TRUE),
+                      sd_AUC = sd(AUC,na.rm=TRUE),
+                      se_AUC = sd_AUC / sqrt(N-1)
+  )
+  
+  write.csv(X_stats_BY,file=paste(outputpath,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE)
+  
+  #calculate the max theoretical L values
+  #only look for max values when K is within 2SD of the ref strain
+  for(q in unique(X$Conc_Num_Factor)){
+    if(q == 0){
+      X_within_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[!is.na(X_within_2SD_K$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) ,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+      X_outside_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[!is.na(X_outside_2SD_K$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1]))  ,]
+      #X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+    }
+    if(q > 0){
+      X_within_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K <- rbind(X_within_2SD_K,X_within_2SD_K_temp)
+      
+      X_outside_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      #X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      X_outside_2SD_K <- rbind(X_outside_2SD_K,X_outside_2SD_K_temp)
+    }
+  }
+  
+  X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                     N = (length(l)),
+                                     mean = mean(l),
+                                     median = median(l),
+                                     max = max(l,na.rm=TRUE),
+                                     min = min(l,na.rm=TRUE),
+                                     sd = sd(l),
+                                     se = sd / sqrt(N-1),
+                                     z_max = (max-mean)/sd
+  )
+  print(X_stats_BY_L_within_2SD_K)
+  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)
+  write.csv(X_stats_BY_L_within_2SD_K,file=paste(outputpath_QC,"Max_Observed_L_Vals_for_spots_within_2SD_K.csv",sep=""),row.names=FALSE)
+  
+  X_stats_BY_L_outside_2SD_K <- ddply(X_outside_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                      N = (length(l)),
+                                      mean = mean(l),
+                                      median = median(l),
+                                      max = max(l,na.rm=TRUE),
+                                      min = min(l,na.rm=TRUE),
+                                      sd = sd(l),
+                                      se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L_outside_2SD_K)
+  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)
+  
+  #X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l,]
+  Outside_2SD_K_L_vs_K <- ggplot(X_outside_2SD_K,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  print(Outside_2SD_K_L_vs_K)
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"RawL_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  Outside_2SD_K_delta_background_vs_K <- ggplot(X_outside_2SD_K,aes(Delta_Backgrd,K,color=as.factor(Conc_Num))) + geom_point(aes(l=l,ORF=ORF,Gene=Gene),shape=3,position="jitter") +
+    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  Outside_2SD_K_delta_background_vs_K
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"DeltaBackground_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  #get the background strain mean values at the no drug conc to calculate shift
+  Background_L <- X_stats_BY_L$mean[1]
+  Background_K <- X_stats_BY_K$mean[1]
+  Background_r <- X_stats_BY_r$mean[1]
+  Background_AUC <- X_stats_BY_AUC$mean[1]
+  
+  #create empty plots for plotting element
+  p_l <-  ggplot()
+  p_K <-  ggplot()
+  p_r <-  ggplot()
+  p_AUC <-  ggplot()
+  
+  p_rf_l <-  ggplot()
+  p_rf_K <-  ggplot()
+  p_rf_r <-  ggplot()
+  p_rf_AUC <-  ggplot()
+  
+  #get only the deletion strains
+  X2 <- X
+  X2 <- X2[X2$OrfRep != "YDL227C",]
+  
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2$Conc_Num))){
+    Concentration <- unique(X2$Conc_Num)[i]
+    X2_temp <- X2[X2$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_new <- X2_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      
+      #X2_temp[X2_temp$K == 0,]$K <- X_stats_ALL_K$max[i]
+      #X2_temp[X2_temp$r == 0,]$r <- X_stats_ALL_r$max[i]
+      #X2_temp[X2_temp$AUC == 0,]$AUC <- X_stats_ALL_AUC$max[i]
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+      
+      X2_new <- rbind(X2_new,X2_temp)
+      
+    }
+  }
+  X2 <- X2_new
+  
+  
+  #get only the RF strains
+  X2_RF <- X
+  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",]
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2_RF$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2_RF$Conc_Num))){
+    Concentration <- unique(X2_RF$Conc_Num)[i]
+    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_RF_new <- X2_RF_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      print(paste("Check loop order, if error, refs have no L values outside theoretical max L, for REFs, conc =",Concentration,sep=" "))
+      
+      X2_RF_new <- rbind(X2_RF_new,X2_RF_temp)
+      
+    }
+  }
+  X2_RF <- X2_RF_new
+  
+  
+  #######Part 4 Get the RF Z score values
+  #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
+  X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_")
+  
+  num_genes_RF <- length(unique(X2_RF$OrfRep))
+  print(num_genes_RF)
+  
+  
+  #create the output data.frame containing columns for each RF strain
+  InteractionScores_RF <- unique(X2_RF["OrfRep"])
+  #InteractionScores_RF$Gene <- unique(X2$Gene)
+  InteractionScores_RF$Gene <- NA
+  InteractionScores_RF$Raw_Shift_L <- NA
+  InteractionScores_RF$Z_Shift_L <- NA
+  InteractionScores_RF$lm_Score_L <- NA
+  InteractionScores_RF$Z_lm_L <- NA  
+  InteractionScores_RF$R_Squared_L <- NA
+  InteractionScores_RF$Sum_Z_Score_L <- NA
+  InteractionScores_RF$Avg_Zscore_L <- NA
+  InteractionScores_RF$Raw_Shift_K <- NA
+  InteractionScores_RF$Z_Shift_K <- NA
+  InteractionScores_RF$lm_Score_K <- NA
+  InteractionScores_RF$Z_lm_K <- NA  
+  InteractionScores_RF$R_Squared_K <- NA
+  InteractionScores_RF$Sum_Z_Score_K <- NA
+  InteractionScores_RF$Avg_Zscore_K <- NA
+  InteractionScores_RF$Raw_Shift_r <- NA
+  InteractionScores_RF$Z_Shift_r <- NA
+  InteractionScores_RF$lm_Score_r <- NA
+  InteractionScores_RF$Z_lm_r <- NA
+  InteractionScores_RF$R_Squared_r <- NA
+  InteractionScores_RF$Sum_Z_Score_r <- NA
+  InteractionScores_RF$Avg_Zscore_r <- NA
+  InteractionScores_RF$Raw_Shift_AUC <- NA
+  InteractionScores_RF$Z_Shift_AUC <- NA
+  InteractionScores_RF$lm_Score_AUC <- NA
+  InteractionScores_RF$Z_lm_AUC <- NA
+  InteractionScores_RF$R_Squared_AUC <- NA
+  InteractionScores_RF$Sum_Z_Score_AUC <- NA
+  InteractionScores_RF$Avg_Zscore_AUC <- NA
+  InteractionScores_RF$NG <- NA
+  InteractionScores_RF$SM <- NA
+  
+  
+  for(i in 1:num_genes_RF){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2_RF$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      gene_lm_AUC <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      gene_interaction_AUC <- NA
+      r_squared_AUC <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction)
+    }
+    
+    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass RF Calculation loop")
+  
+  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  print(paste("Mean RF linear regression score L",lm_mean_L))
+  
+  
+  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L)/(lm_sd_L)
+  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K)/(lm_sd_K)
+  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r)/(lm_sd_r)
+  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC)/(lm_sd_AUC)
+  
+  
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L,decreasing=TRUE),]
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG,decreasing=TRUE),]
+  write.csv(InteractionScores_RF,paste(outputpath,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  
+  for(i in 1:num_genes_RF){
+    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel,]
+    
+    p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg Zscore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm Zscore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_rf_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final,X_ZCalculations)
+    }
+  }
+  print("Pass RF ggplot loop")
+  write.csv(X_stats_interaction_ALL_RF_final,paste(outputpath,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  ####### Part 5 - Get Zscores for Gene deletion strains
+  
+  
+  
+  #get total number of genes for the next loop
+  num_genes <- length(unique(X2$OrfRep))
+  print(num_genes)
+  
+  #create the output data.frame containing columns for each deletion strain
+  InteractionScores <- unique(X2["OrfRep"])
+  #InteractionScores$Gene <- unique(X2$Gene)
+  InteractionScores$Gene <- NA
+  InteractionScores$Raw_Shift_L <- NA
+  InteractionScores$Z_Shift_L <- NA
+  InteractionScores$lm_Score_L <- NA
+  InteractionScores$Z_lm_L <- NA
+  InteractionScores$R_Squared_L <- NA
+  InteractionScores$Sum_Z_Score_L <- NA
+  InteractionScores$Avg_Zscore_L <- NA
+  InteractionScores$Raw_Shift_K <- NA
+  InteractionScores$Z_Shift_K <- NA
+  InteractionScores$lm_Score_K <- NA
+  InteractionScores$Z_lm_K <- NA
+  InteractionScores$R_Squared_K <- NA
+  InteractionScores$Sum_Z_Score_K <- NA
+  InteractionScores$Avg_Zscore_K <- NA
+  InteractionScores$Raw_Shift_r <- NA
+  InteractionScores$Z_Shift_r <- NA
+  InteractionScores$lm_Score_r <- NA
+  InteractionScores$Z_lm_r <- NA
+  InteractionScores$R_Squared_r <- NA
+  InteractionScores$Sum_Z_Score_r <- NA
+  InteractionScores$Avg_Zscore_r <- NA
+  InteractionScores$Raw_Shift_AUC <- NA
+  InteractionScores$Z_Shift_AUC <- NA
+  InteractionScores$lm_Score_AUC <- NA
+  InteractionScores$Z_lm_AUC <- NA
+  InteractionScores$R_Squared_AUC <- NA
+  InteractionScores$Sum_Z_Score_AUC <- NA
+  InteractionScores$Avg_Zscore_AUC <- NA
+  InteractionScores$NG <- NA
+  InteractionScores$DB <- NA
+  InteractionScores$SM <- NA
+  
+  for(i in 1:num_genes){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_L-lm_mean_L)/lm_sd_L
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_K-lm_mean_K)/lm_sd_K
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_r-lm_mean_r)/lm_sd_r
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_AUC-lm_mean_AUC)/lm_sd_AUC
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-NA
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction)
+    }
+    
+    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass Int Calculation loop")
+  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L,decreasing=TRUE),]
+  InteractionScores <- InteractionScores[order(InteractionScores$NG,decreasing=TRUE),]
+  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
+  #X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG,decreasing=TRUE),]
+  write.csv(InteractionScores,paste(outputpath,"ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_K <- InteractionScores[InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores_deletion_enhancers_L[!is.na(InteractionScores_deletion_enhancers_L$OrfRep),]
+  InteractionScores_deletion_enhancers_K <- InteractionScores_deletion_enhancers_K[!is.na(InteractionScores_deletion_enhancers_K$OrfRep),]
+  InteractionScores_deletion_suppressors_L <- InteractionScores_deletion_suppressors_L[!is.na(InteractionScores_deletion_suppressors_L$OrfRep),]
+  InteractionScores_deletion_suppressors_K <- InteractionScores_deletion_suppressors_K[!is.na(InteractionScores_deletion_suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores_deletion_enhancers_and_Suppressors_L[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores_deletion_enhancers_and_Suppressors_K[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE)
+  
+  #get enhancers and suppressors for linear regression
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2,]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores[InteractionScores$Z_lm_K <= -2,]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores_deletion_enhancers_L_lm[!is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores_deletion_enhancers_K_lm[!is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores_deletion_suppressors_L_lm[!is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores_deletion_suppressors_K_lm[!is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores_deletion_enhancers_and_Suppressors_L_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores_deletion_enhancers_and_Suppressors_K_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE)
+  
+  
+  write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+  print('ln 1570 write StudyInfo.csv after ')
+        #write.table(Labels,file=paste("../Code/StudyInfo.txt"),sep="\t",row.names = FALSE)
+  
+  for(i in 1:num_genes){
+    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel,]
+    
+    p_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_K,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final,X_ZCalculations)
+    }
+  }
+  print("Pass Int ggplot loop")
+  write.csv(X_stats_interaction_ALL_final,paste(outputpath,"ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  
+  
+  
+  
+  Blank <- ggplot(X2_RF) + geom_blank()
+  
+  pdf(paste(outputpath,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_l[[num]],p_K[[num]],p_r[[num]],p_AUC[[num]],p_l[[num+1]],p_K[[num+1]],p_r[[num+1]],p_AUC[[num+1]],p_l[[num+2]],p_K[[num+2]],p_r[[num+2]],p_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_l[[364]],p_K[[364]],p_r[[364]],p_l[[365]],p_K[[365]],p_r[[365]],p_l[[366]],p_K[[366]],p_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_K[[num+3]],p_r[[num+3]],p1[[num+4]],p_K[[num+4]],p_r[[num+4]]
+  }
+  if(num_genes != (num+2)){
+    total_num = num_genes - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],p_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  
+  
+  pdf(paste(outputpath,"RF_InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(12000,0)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes_RF)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes_RF)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_rf_l[[num]],p_rf_K[[num]],p_rf_r[[num]],p_rf_AUC[[num]],p_rf_l[[num+1]],p_rf_K[[num+1]],p_rf_r[[num+1]],p_rf_AUC[[num+1]],p_rf_l[[num+2]],p_rf_K[[num+2]],p_rf_r[[num+2]],p_rf_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_rf_l[[364]],p_rf_K[[364]],p_rf_r[[364]],p_rf_l[[365]],p_rf_K[[365]],p_rf_r[[365]],p_rf_l[[366]],p_rf_K[[366]],p_rf_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p1[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]]
+  }
+  if(num_genes_RF != (num+2)){
+    total_num = num_genes_RF - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],p_rf_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  #print rank plots for L and K gene interactions
+  
+  
+  InteractionScores_AdjustMissing <- InteractionScores
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L),]$Avg_Zscore_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K),]$Avg_Zscore_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r),]$Avg_Zscore_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC),]$Avg_Zscore_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank <- NA
+  InteractionScores_AdjustMissing$K_Rank <- NA
+  InteractionScores_AdjustMissing$r_Rank <- NA
+  InteractionScores_AdjustMissing$AUC_Rank <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
+  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
+  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
+  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
+  
+  #
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L),]$Z_lm_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K),]$Z_lm_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r),]$Z_lm_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC),]$Z_lm_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- NA
+  InteractionScores_AdjustMissing$K_Rank_lm <- NA
+  InteractionScores_AdjustMissing$r_Rank_lm <- NA
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
+  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
+  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
+  
+  
+  
+  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  
+  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,]
+  
+  #find overlaps
+  X_NArm$Overlap <- "No Effect"
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Both")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Both")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2,]$Overlap <- "Deletion Enhancer lm only")
+  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2,]$Overlap <- "Deletion Suppressor lm only")
+  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L <- lm(X_NArm$Z_lm_L~X_NArm$Avg_Zscore_L)
+  L_lm <- summary(get_lm_L)
+  
+  get_lm_K <- lm(X_NArm$Z_lm_K~X_NArm$Avg_Zscore_K)
+  K_lm <- summary(get_lm_K)
+  
+  get_lm_r <- lm(X_NArm$Z_lm_r~X_NArm$Avg_Zscore_r)
+  r_lm <- summary(get_lm_r)
+  
+  get_lm_AUC <- lm(X_NArm$Z_lm_AUC~X_NArm$Avg_Zscore_AUC)
+  AUC_lm <- summary(get_lm_AUC)
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm L") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_K,Z_lm_K)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm K") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(K_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_r,Z_lm_r)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm r") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(r_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_AUC,Z_lm_AUC)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm AUC") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(AUC_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  dev.off()
+  
+  
+  lm_v_Zscore_L <- ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L,ORF=OrfRep,Gene=Gene,NG=NG,SM=SM,DB=DB)) + geom_point(aes(color=Overlap),shape=3) + 
+    geom_smooth(method = "lm",color=1) +  ggtitle("Avg Zscore vs lm L") +
+    geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+    annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right()
+  
+  pgg <- ggplotly(lm_v_Zscore_L)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath,"Avg_Zscore_vs_lm_NA_rm.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
+  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
+  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
+  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
+  
+  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
+  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
+  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
+  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L2 <- lm(X_NArm$L_Rank_lm~X_NArm$L_Rank)
+  L_lm2 <- summary(get_lm_L2)
+  
+  get_lm_K2 <- lm(X_NArm$K_Rank_lm~X_NArm$K_Rank)
+  K_lm2 <- summary(get_lm_K2)
+  
+  get_lm_r2 <- lm(X_NArm$r_Rank_lm~X_NArm$r_Rank)
+  r_lm2 <- summary(get_lm_r2)
+  
+  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm~X_NArm$AUC_Rank)
+  AUC_lm2 <- summary(get_lm_AUC2)
+  
+  num_genes_NArm2 <- (dim(X_NArm)[1])/2
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_ranked_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm L") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(K_Rank,K_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm K") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(K_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(r_Rank,r_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm r") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(r_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(AUC_Rank,AUC_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank of Avg Zscore vs lm AUC") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(AUC_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  
+  
+  dev.off()
+  
+  
+  
+  Rank_L_1SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+}
+
+
+
+#get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+get_lm_1 <- lm(X_NArm$Z_lm_K~X_NArm$Z_lm_L)
+L_lm_1 <- summary(get_lm_1)
+
+get_lm_2 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_L)
+L_lm_2 <- summary(get_lm_2)
+
+get_lm_3 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_L)
+L_lm_3 <- summary(get_lm_3)
+
+get_lm_4 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_K)
+L_lm_4 <- summary(get_lm_4)
+
+get_lm_5 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_K)
+L_lm_5 <- summary(get_lm_5)
+
+get_lm_6 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_r)
+L_lm_6 <- summary(get_lm_6)
+
+
+pdf(file=paste(outputpath,"Correlation_CPPs.pdf",sep=""),width = 10, height = 7, onefile = TRUE)
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L),]
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_K),color="cyan") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_r),color="cyan") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_r),color="cyan") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_r,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+
+
+
+
+dev.off()
+
+
+#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE)
+timestamp()
+
+#BoneYard***********************************************
+#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced.
+#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. 
+#Corruptable but better than nothing.
+#if(is.na(Args[2])){
+# std=3
+#}else {
+# std= Arg[2]
+#Delta_Background_sdFactor <- 2  #Args[3]
+#DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#} 
+
diff --git a/workflow/.old/templates/qhtcp/Exp2/backups/InteractTemplateB4Prompt4SDinput.R b/workflow/.old/templates/qhtcp/Exp2/backups/InteractTemplateB4Prompt4SDinput.R
new file mode 100644
index 00000000..3b11777c
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp2/backups/InteractTemplateB4Prompt4SDinput.R
@@ -0,0 +1,2702 @@
+#Based on InteractionTemplate.R which is based on Sean Santose's Interaction_V5 script.
+#Adapt SS For Structured Data storage but using command line scripts
+###Set up the required libraries, call required plot theme elements and set up the command line arguments
+library("ggplot2")
+library("plyr")
+library("extrafont")
+library("gridExtra")
+library("gplots")
+library("RColorBrewer")
+library("stringr")
+#library("gdata")
+library(plotly)
+library(htmlwidgets)
+
+Args <- commandArgs(TRUE)
+input_file <- Args[1] #"!!Results_17_0827_yor1null-rpl12anull misLabeledAsFrom MI 17_0919_yor1-curated.txt"  #Args[1]  #Arg 1 #"!!ResultsStd_JS 19_1224_HLEG_P53.txt" is the !!results ... .txt
+
+#Path to Output Directory
+W=getwd() #R is F'd up, Can't use, Any legitamate platfold could build out dirs from this
+outDir <- "ZScores/" #"Args[2] #paste0(W,"/ZScores/")
+subDir <- outDir     #Args[2]
+
+if (file.exists(subDir)){
+  outputpath <- subDir
+} else {
+  dir.create(file.path(subDir))
+}
+
+if (file.exists(paste(subDir,"QC/",sep=""))){
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+} else {
+  dir.create(file.path(paste(subDir,"QC/",sep="")))
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+}
+#define the output path (formerly the second argument from Rscript)
+outputpath <- outDir 
+
+#Set Args[2] the Background contamination noise filter as a function of standard deviation
+#Sean recommends 3 or 5 SD factor.
+#Capture Exp_ number,use it to Save Args[2]{std}to Labels field and then Write to Labels to studyInfo.txt for future reference
+Labels <- read.csv(file= "../Code/StudyInfo.csv",stringsAsFactors = FALSE)  #,sep= ",")
+print("Be sure to include Argument 2 the Bacground noise filter standard deviation i.e., 3 or 5 per Sean")
+std= as.numeric(Args[2])
+expNumber<- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd()))
+Labels[expNumber,3]= as.numeric(std)
+Delta_Background_sdFactor <- std
+DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#Write Background SD value to studyInfo.txt file
+#write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+print('ln 50 write StudyInfo.csv ')
+#write.table(Labels,file=paste(outputpath,"StudyInfo.txt"),sep = "\t",row.names = FALSE)
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++BEGIN USER DATA SELECTION SECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+#read in the data
+X <- read.delim(input_file,skip=2,as.is=T,row.names=1,strip.white=TRUE)
+X <- X[!(X[[1]]%in%c("","Scan")),]
+#X <- X[!(X[[1]]%in%c(61:76)),]  #Remove dAmp plates which are Scans 61 thru 76
+
+#X <- X[which(X$Specifics == "WT"),]
+
+X_length <- length(X[1,])
+X_end <- length(X[1,]) - 2
+X <- X[,c(1:46,X_end:X_length)]
+
+
+#use numeric data to perform operations
+X$Col <- as.numeric(X$Col)
+X$Row <- as.numeric(X$Row)
+X$l <- as.numeric(X$l)
+X$K <- as.numeric(X$K)
+X$r <- as.numeric(X$r)
+X$Scan <- as.numeric(X$Scan)
+X$AUC <- as.numeric(X$AUC)
+X$LstBackgrd <- as.numeric(X$LstBackgrd)
+X$X1stBackgrd <- as.numeric(X$X1stBackgrd)
+
+#set the OrfRep to YDL227C for the ref data
+X[X$ORF == "YDL227C",]$OrfRep <- "YDL227C"
+#Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. 
+#That is the first DM plates are removed from the data set with the following.
+X <- X[X$Conc1 != "0ug/mL",] #This occurs only for Exp1 and Exp2 and so doesn't have any effect on Exp3&4
+
+
+#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. 
+#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used.
+#try(X <- X[X$ORF != "bad_spot",])
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=
+
+#get total number of drug concentrations
+Total_Conc_Nums <- length(unique(X$Conc))
+
+#function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
+numextract <- function(string){ 
+  str_extract(string, "\\-*\\d+\\.*\\d*")
+} 
+
+#generate a new column with the numeric drug concs
+X$Conc_Num <- as.numeric(numextract(X$Conc))
+#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
+X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1
+
+#Get the max factor for concentration
+MAX_CONC <- max(X$Conc_Num_Factor)
+#if treating numbers not as factors uncomment next line and comment out previous line
+#MAX_CONC <- max(X$Conc_Num)
+
+#remove wells with problems for making graphs and to not include in summary statistics
+X <- X[X$Gene != "BLANK",]
+X <- X[X$Gene != "Blank",] 
+X <- X[X$ORF != "Blank",]
+X <- X[X$Gene != "blank",]
+#X <- X[X$Gene != "HO",]
+Xbu= X
+#Inserted to use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script). This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
+#Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to write over the RF1&RF2 geneNames which caused a variance with his code results 
+#in the Z_lm_L,K,r&AUC output values. Values correlated well but were off by a multiplier factor.
+SGDgeneList= "../Code/SGD_features.tab"
+genes = data.frame(read.delim(file=SGDgeneList,quote="",header=FALSE,colClasses = c(rep("NULL",3), rep("character", 2), rep("NULL", 11))))
+for(i in 1:length(X[,14])){
+  ii= as.numeric(i)
+  line_num = match(X[ii,14],genes[,1],nomatch=1)
+  OrfRepColNum= as.numeric(match('OrfRep',names(X)))
+  if(X[ii,OrfRepColNum]!= "YDL227C"){
+     X[ii,15] = genes[line_num,2]
+  }
+  if((X[ii,15] == "")||(X[ii,15] == "OCT1")){
+    X[ii,15] = X[ii,OrfRepColNum]
+  }
+}
+Xblankreplace= X
+#X= Xbu #for restore testing restore X if geneName 'Match' routine needs changing
+
+#Remove dAmPs *******************************
+DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
+Damps <- read.delim(DAmPs_List,header=F)
+
+X <- X[!(X$ORF %in% Damps$V1),]  #fix this to Damps[,1]
+XafterDampsRM=X  #Backup for debugging especially when Rstudio goes crazy out of control
+# ***********
+
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++END USER DATA SELECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+print("ln137 End of User Section including blank gene writeOver")
+#++++Begin Graphics Boiler Plate Section+++++++++++++++++++++++++++++++++++++++
+#theme elements for plots
+theme_Publication <- function(base_size=14, base_family="sans") {
+  library(grid)
+  library(ggthemes)
+  (theme_foundation(base_size=base_size, base_family=base_family)
+    + theme(plot.title = element_text(face = "bold",
+                                      size = rel(1.2), hjust = 0.5),
+            text = element_text(),
+            panel.background = element_rect(colour = NA),
+            plot.background = element_rect(colour = NA),
+            panel.border = element_rect(colour = NA),
+            axis.title = element_text(face = "bold",size = rel(1)),
+            axis.title.y = element_text(angle=90,vjust =2),
+            axis.title.x = element_text(vjust = -0.2),
+            axis.text = element_text(), 
+            axis.line = element_line(colour="black"),
+            axis.ticks = element_line(),
+            panel.grid.major = element_line(colour="#f0f0f0"),
+            panel.grid.minor = element_blank(),
+            legend.key = element_rect(colour = NA),
+            legend.position = "bottom",
+            legend.direction = "horizontal",
+            legend.key.size= unit(0.2, "cm"),
+            legend.spacing = unit(0, "cm"),
+            legend.title = element_text(face="italic"),
+            plot.margin=unit(c(10,5,5,5),"mm"),
+            strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+            strip.text = element_text(face="bold")
+    ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  library(scales)
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
+  (theme_foundation(base_size=base_size, base_family=base_family)
+   + theme(plot.title = element_text(face = "bold",
+                                     size = rel(1.2), hjust = 0.5),
+           text = element_text(),
+           panel.background = element_rect(colour = NA),
+           plot.background = element_rect(colour = NA),
+           panel.border = element_rect(colour = NA),
+           axis.title = element_text(face = "bold",size = rel(1)),
+           axis.title.y = element_text(angle=90,vjust =2),
+           axis.title.x = element_text(vjust = -0.2),
+           axis.text = element_text(), 
+           axis.line = element_line(colour="black"),
+           axis.ticks = element_line(),
+           panel.grid.major = element_line(colour="#f0f0f0"),
+           panel.grid.minor = element_blank(),
+           legend.key = element_rect(colour = NA),
+           legend.position = "right",
+           legend.direction = "vertical",
+           legend.key.size= unit(0.5, "cm"),
+           legend.spacing = unit(0, "cm"),
+           legend.title = element_text(face="italic"),
+           plot.margin=unit(c(10,5,5,5),"mm"),
+           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+           strip.text = element_text(face="bold")
+   ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+#print timestamp for initial time the code starts
+timestamp()
+#+++++BEGIN QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+###Part 2 - Quality control
+#print quality control graphs for each dataset before removing data due to contamination
+#and before adjusting missing data to max theoretical values
+
+#plate analysis plot
+#plate analysis is a quality check to identify plate effects containing anomalies
+
+Plate_Analysis_L <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+
+
+Plate_Analysis_L_Box <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K_Box <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r_Box <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+#quality control - values with a high delta background likely have heavy contamination
+#check the frequency of these values
+#report the L and K values of these spots
+#report the number to be removed based on the Delta_Background_Tolerance
+X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd
+
+
+#raw l vs K before QC
+Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle("Raw L vs K before QC") +
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8)
+Raw_l_vs_K_beforeQC
+dev.off()
+pgg <- ggplotly(Raw_l_vs_K_beforeQC)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_beforeQC.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+
+#set delta background tolerance based on 3 sds from the mean delta background
+Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(DelBGFactr*sd(X$Delta_Backgrd))
+#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
+print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" "))
+
+Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+
+
+X_Delta_Backgrd_above_Tolerance <- X[X$Delta_Backgrd >= Delta_Background_Tolerance,]
+
+X_Delta_Backgrd_above_Tolerance_K_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$K,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_L_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$l,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_toRemove <- dim(X_Delta_Backgrd_above_Tolerance)[1]
+
+X_Delta_Backgrd_above_Tolerance_L_vs_K <- ggplot(X_Delta_Backgrd_above_Tolerance,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle(paste("Raw L vs K for strains above delta background threshold of",Delta_Background_Tolerance,"or above")) +
+  annotate("text",x=X_Delta_Backgrd_above_Tolerance_L_halfmedian,y=X_Delta_Backgrd_above_Tolerance_K_halfmedian,
+           label = paste("Strains above delta background tolerance = ",X_Delta_Backgrd_above_Tolerance_toRemove)) + 
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8)
+X_Delta_Backgrd_above_Tolerance_L_vs_K
+dev.off()
+pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+#frequency plot for all data vs. the delta_background
+DeltaBackground_Frequency_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_density() +
+  ggtitle("Density plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+#bar plot for all data vs. the delta_background
+DeltaBackground_Bar_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_bar() +
+  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+pdf(file = paste(outputpath_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8)
+print(DeltaBackground_Frequency_Plot)
+print(DeltaBackground_Bar_Plot)
+dev.off()
+
+
+#Need to identify missing data, and differentiate between this data and removed data so the removed data can get set to NA and the missing data can get set to max theoretical values
+#1 for missing data, 0 for non missing data
+#Use "NG" for NoGrowth rather than "missing"
+X$NG <- 0
+try(X[X$l == 0 & !is.na(X$l),]$NG <- 1)
+
+#1 for removed data, 0 non removed data
+#Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
+X$DB <- 0
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$DB <- 1)
+
+#replace the CPPs for l, r, AUC and K (must be last!) for removed data
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$l <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$r <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$AUC <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$K <- NA)
+
+
+Plate_Analysis_L_afterQC <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L
+Plate_Analysis_L_afterQC
+Plate_Analysis_K
+Plate_Analysis_K_afterQC
+Plate_Analysis_r
+Plate_Analysis_r_afterQC
+Plate_Analysis_AUC
+Plate_Analysis_AUC_afterQC
+Plate_Analysis_Delta_Backgrd
+Plate_Analysis_Delta_Backgrd_afterQC
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box
+Plate_Analysis_L_Box_afterQC
+Plate_Analysis_K_Box
+Plate_Analysis_K_Box_afterQC
+Plate_Analysis_r_Box
+Plate_Analysis_r_Box_afterQC
+Plate_Analysis_AUC_Box
+Plate_Analysis_AUC_Box_afterQC
+Plate_Analysis_Delta_Backgrd_Box
+Plate_Analysis_Delta_Backgrd_Box_afterQC
+dev.off()
+
+#remove the zero values and print plate analysis
+X_noZero <- X[which(X$l > 0),]
+Plate_Analysis_L_afterQC_Z <- ggplot(X_noZero,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC_Z <- ggplot(X_noZero,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC_Z <- ggplot(X_noZero,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC_Z <- ggplot(X_noZero,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC_Z <- ggplot(X_noZero,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L_afterQC_Z
+Plate_Analysis_K_afterQC_Z
+Plate_Analysis_r_afterQC_Z
+Plate_Analysis_AUC_afterQC_Z
+Plate_Analysis_Delta_Backgrd_afterQC_Z
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box_afterQC_Z
+Plate_Analysis_K_Box_afterQC_Z
+Plate_Analysis_r_Box_afterQC_Z
+Plate_Analysis_AUC_Box_afterQC_Z
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
+dev.off()
+
+#remove dataset with zeros removed
+rm(X_noZero)
+
+
+#X_test_missing_and_removed <- X[X$Removed == 1,]
+
+#calculate summary statistics for all strains, including both background and the deletions
+X_stats_ALL <- ddply(X, c("Conc_Num","Conc_Num_Factor"), summarise,
+                     N = (length(l)),
+                     mean_L = mean(l,na.rm=TRUE),
+                     median_L = median(l,na.rm=TRUE),
+                     max_L = max(l,na.rm=TRUE),
+                     min_L = min(l,na.rm=TRUE),
+                     sd_L = sd(l,na.rm=TRUE),
+                     se_L = sd_L / sqrt(N-1),
+                     mean_K = mean(K,na.rm=TRUE),
+                     median_K = median(K,na.rm=TRUE),
+                     max_K = max(K,na.rm=TRUE),
+                     min_K = min(K,na.rm=TRUE),
+                     sd_K = sd(K,na.rm=TRUE),
+                     se_K = sd_K / sqrt(N-1),
+                     mean_r = mean(r,na.rm=TRUE),
+                     median_r = median(r,na.rm=TRUE),
+                     max_r = max(r,na.rm=TRUE),
+                     min_r = min(r,na.rm=TRUE),
+                     sd_r = sd(r,na.rm=TRUE),
+                     se_r = sd_r / sqrt(N-1),
+                     mean_AUC = mean(AUC,na.rm=TRUE),
+                     median_AUC = median(AUC,na.rm=TRUE),
+                     max_AUC = max(AUC,na.rm=TRUE),
+                     min_AUC = min(AUC,na.rm=TRUE),
+                     sd_AUC = sd(AUC,na.rm=TRUE),
+                     se_AUC = sd_AUC / sqrt(N-1)
+)
+#print(X_stats_ALL_L)
+
+write.csv(X_stats_ALL,file=paste(outputpath,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE)
+#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+##### Part 3 - Generate summary statistics and calculate the max theoretical L value
+##### Calculate the Z score at each drug conc for each deletion strain
+
+
+#get the background strains - can be modified to take another argument but for most screens will just be YDL227C
+Background_Strains <- c("YDL227C")
+
+#first part of loop will go through for each background strain 
+#most cases there will only be one YDL227C
+for(s in Background_Strains){
+  X_Background <- X[X$OrfRep == s,]
+  
+  #if there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
+  #we may want to consider in some cases giving the max high value to L depending on the data type
+  if(table(X_Background$l)[1] == 0){
+    X_Background[X_Background$l == 0,]$l <- NA
+    X_Background[X_Background$K == 0,]$K <- NA
+    X_Background[X_Background$r == 0,]$r <- NA
+    X_Background[X_Background$AUC == 0,]$AUC <- NA
+  }
+  
+  X_Background <- X_Background[!is.na(X_Background$l),]
+  
+  #get summary stats for L, K, R, AUC
+  X_stats_BY_L <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(l)),
+                        mean = mean(l,na.rm=TRUE),
+                        median = median(l,na.rm=TRUE),
+                        max = max(l,na.rm=TRUE),
+                        min = min(l,na.rm=TRUE),
+                        sd = sd(l,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L)
+  X1_SD <- max(X_stats_BY_L$sd)
+  
+  X_stats_BY_K <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(K)),
+                        mean = mean(K,na.rm=TRUE),
+                        median = median(K,na.rm=TRUE),
+                        max = max(K,na.rm=TRUE),
+                        min = min(K,na.rm=TRUE),
+                        sd = sd(K,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_K <- max(X_stats_BY_K$sd)
+  
+  
+  X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = length(r),
+                        mean = mean(r,na.rm=TRUE),
+                        median = median(r,na.rm=TRUE),
+                        max = max(r,na.rm=TRUE),
+                        min = min(r,na.rm=TRUE),
+                        sd = sd(r,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_r <- max(X_stats_BY_r$sd)
+  
+  X_stats_BY_AUC <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                          N = length(AUC),
+                          mean = mean(AUC,na.rm=TRUE),
+                          median = median(AUC,na.rm=TRUE),
+                          max = max(AUC,na.rm=TRUE),
+                          min = min(AUC,na.rm=TRUE),
+                          sd = sd(AUC,na.rm=TRUE),
+                          se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
+  
+  X_stats_BY <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                      N = (length(l)),
+                      mean_L = mean(l,na.rm=TRUE),
+                      median_L = median(l,na.rm=TRUE),
+                      max_L = max(l,na.rm=TRUE),
+                      min_L = min(l,na.rm=TRUE),
+                      sd_L = sd(l,na.rm=TRUE),
+                      se_L = sd_L / sqrt(N-1),
+                      mean_K = mean(K,na.rm=TRUE),
+                      median_K = median(K,na.rm=TRUE),
+                      max_K = max(K,na.rm=TRUE),
+                      min_K = min(K,na.rm=TRUE),
+                      sd_K = sd(K,na.rm=TRUE),
+                      se_K = sd_K / sqrt(N-1),
+                      mean_r = mean(r,na.rm=TRUE),
+                      median_r = median(r,na.rm=TRUE),
+                      max_r = max(r,na.rm=TRUE),
+                      min_r = min(r,na.rm=TRUE),
+                      sd_r = sd(r,na.rm=TRUE),
+                      se_r = sd_r / sqrt(N-1),
+                      mean_AUC = mean(AUC,na.rm=TRUE),
+                      median_AUC = median(AUC,na.rm=TRUE),
+                      max_AUC = max(AUC,na.rm=TRUE),
+                      min_L = min(AUC,na.rm=TRUE),
+                      sd_AUC = sd(AUC,na.rm=TRUE),
+                      se_AUC = sd_AUC / sqrt(N-1)
+  )
+  
+  write.csv(X_stats_BY,file=paste(outputpath,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE)
+  
+  #calculate the max theoretical L values
+  #only look for max values when K is within 2SD of the ref strain
+  for(q in unique(X$Conc_Num_Factor)){
+    if(q == 0){
+      X_within_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[!is.na(X_within_2SD_K$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) ,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+      X_outside_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[!is.na(X_outside_2SD_K$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1]))  ,]
+      #X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+    }
+    if(q > 0){
+      X_within_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K <- rbind(X_within_2SD_K,X_within_2SD_K_temp)
+      
+      X_outside_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      #X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      X_outside_2SD_K <- rbind(X_outside_2SD_K,X_outside_2SD_K_temp)
+    }
+  }
+  
+  X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                     N = (length(l)),
+                                     mean = mean(l),
+                                     median = median(l),
+                                     max = max(l,na.rm=TRUE),
+                                     min = min(l,na.rm=TRUE),
+                                     sd = sd(l),
+                                     se = sd / sqrt(N-1),
+                                     z_max = (max-mean)/sd
+  )
+  print(X_stats_BY_L_within_2SD_K)
+  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)
+  write.csv(X_stats_BY_L_within_2SD_K,file=paste(outputpath_QC,"Max_Observed_L_Vals_for_spots_within_2SD_K.csv",sep=""),row.names=FALSE)
+  
+  X_stats_BY_L_outside_2SD_K <- ddply(X_outside_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                      N = (length(l)),
+                                      mean = mean(l),
+                                      median = median(l),
+                                      max = max(l,na.rm=TRUE),
+                                      min = min(l,na.rm=TRUE),
+                                      sd = sd(l),
+                                      se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L_outside_2SD_K)
+  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)
+  
+  #X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l,]
+  Outside_2SD_K_L_vs_K <- ggplot(X_outside_2SD_K,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  print(Outside_2SD_K_L_vs_K)
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"RawL_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  Outside_2SD_K_delta_background_vs_K <- ggplot(X_outside_2SD_K,aes(Delta_Backgrd,K,color=as.factor(Conc_Num))) + geom_point(aes(l=l,ORF=ORF,Gene=Gene),shape=3,position="jitter") +
+    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  Outside_2SD_K_delta_background_vs_K
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"DeltaBackground_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  #get the background strain mean values at the no drug conc to calculate shift
+  Background_L <- X_stats_BY_L$mean[1]
+  Background_K <- X_stats_BY_K$mean[1]
+  Background_r <- X_stats_BY_r$mean[1]
+  Background_AUC <- X_stats_BY_AUC$mean[1]
+  
+  #create empty plots for plotting element
+  p_l <-  ggplot()
+  p_K <-  ggplot()
+  p_r <-  ggplot()
+  p_AUC <-  ggplot()
+  
+  p_rf_l <-  ggplot()
+  p_rf_K <-  ggplot()
+  p_rf_r <-  ggplot()
+  p_rf_AUC <-  ggplot()
+  
+  #get only the deletion strains
+  X2 <- X
+  X2 <- X2[X2$OrfRep != "YDL227C",]
+  
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2$Conc_Num))){
+    Concentration <- unique(X2$Conc_Num)[i]
+    X2_temp <- X2[X2$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_new <- X2_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      
+      #X2_temp[X2_temp$K == 0,]$K <- X_stats_ALL_K$max[i]
+      #X2_temp[X2_temp$r == 0,]$r <- X_stats_ALL_r$max[i]
+      #X2_temp[X2_temp$AUC == 0,]$AUC <- X_stats_ALL_AUC$max[i]
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+      
+      X2_new <- rbind(X2_new,X2_temp)
+      
+    }
+  }
+  X2 <- X2_new
+  
+  
+  #get only the RF strains
+  X2_RF <- X
+  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",]
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2_RF$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2_RF$Conc_Num))){
+    Concentration <- unique(X2_RF$Conc_Num)[i]
+    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_RF_new <- X2_RF_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      print(paste("Check loop order, if error, refs have no L values outside theoretical max L, for REFs, conc =",Concentration,sep=" "))
+      
+      X2_RF_new <- rbind(X2_RF_new,X2_RF_temp)
+      
+    }
+  }
+  X2_RF <- X2_RF_new
+  
+  
+  #######Part 4 Get the RF Z score values
+  #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
+  X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_")
+  
+  num_genes_RF <- length(unique(X2_RF$OrfRep))
+  print(num_genes_RF)
+  
+  
+  #create the output data.frame containing columns for each RF strain
+  InteractionScores_RF <- unique(X2_RF["OrfRep"])
+  #InteractionScores_RF$Gene <- unique(X2$Gene)
+  InteractionScores_RF$Gene <- NA
+  InteractionScores_RF$Raw_Shift_L <- NA
+  InteractionScores_RF$Z_Shift_L <- NA
+  InteractionScores_RF$lm_Score_L <- NA
+  InteractionScores_RF$Z_lm_L <- NA  
+  InteractionScores_RF$R_Squared_L <- NA
+  InteractionScores_RF$Sum_Z_Score_L <- NA
+  InteractionScores_RF$Avg_Zscore_L <- NA
+  InteractionScores_RF$Raw_Shift_K <- NA
+  InteractionScores_RF$Z_Shift_K <- NA
+  InteractionScores_RF$lm_Score_K <- NA
+  InteractionScores_RF$Z_lm_K <- NA  
+  InteractionScores_RF$R_Squared_K <- NA
+  InteractionScores_RF$Sum_Z_Score_K <- NA
+  InteractionScores_RF$Avg_Zscore_K <- NA
+  InteractionScores_RF$Raw_Shift_r <- NA
+  InteractionScores_RF$Z_Shift_r <- NA
+  InteractionScores_RF$lm_Score_r <- NA
+  InteractionScores_RF$Z_lm_r <- NA
+  InteractionScores_RF$R_Squared_r <- NA
+  InteractionScores_RF$Sum_Z_Score_r <- NA
+  InteractionScores_RF$Avg_Zscore_r <- NA
+  InteractionScores_RF$Raw_Shift_AUC <- NA
+  InteractionScores_RF$Z_Shift_AUC <- NA
+  InteractionScores_RF$lm_Score_AUC <- NA
+  InteractionScores_RF$Z_lm_AUC <- NA
+  InteractionScores_RF$R_Squared_AUC <- NA
+  InteractionScores_RF$Sum_Z_Score_AUC <- NA
+  InteractionScores_RF$Avg_Zscore_AUC <- NA
+  InteractionScores_RF$NG <- NA
+  InteractionScores_RF$SM <- NA
+  
+  
+  for(i in 1:num_genes_RF){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2_RF$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      gene_lm_AUC <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      gene_interaction_AUC <- NA
+      r_squared_AUC <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction)
+    }
+    
+    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass RF Calculation loop")
+  
+  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  print(paste("Mean RF linear regression score L",lm_mean_L))
+  
+  
+  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L)/(lm_sd_L)
+  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K)/(lm_sd_K)
+  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r)/(lm_sd_r)
+  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC)/(lm_sd_AUC)
+  
+  
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L,decreasing=TRUE),]
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG,decreasing=TRUE),]
+  write.csv(InteractionScores_RF,paste(outputpath,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  
+  for(i in 1:num_genes_RF){
+    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel,]
+    
+    p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg Zscore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm Zscore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_rf_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final,X_ZCalculations)
+    }
+  }
+  print("Pass RF ggplot loop")
+  write.csv(X_stats_interaction_ALL_RF_final,paste(outputpath,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  ####### Part 5 - Get Zscores for Gene deletion strains
+  
+  
+  
+  #get total number of genes for the next loop
+  num_genes <- length(unique(X2$OrfRep))
+  print(num_genes)
+  
+  #create the output data.frame containing columns for each deletion strain
+  InteractionScores <- unique(X2["OrfRep"])
+  #InteractionScores$Gene <- unique(X2$Gene)
+  InteractionScores$Gene <- NA
+  InteractionScores$Raw_Shift_L <- NA
+  InteractionScores$Z_Shift_L <- NA
+  InteractionScores$lm_Score_L <- NA
+  InteractionScores$Z_lm_L <- NA
+  InteractionScores$R_Squared_L <- NA
+  InteractionScores$Sum_Z_Score_L <- NA
+  InteractionScores$Avg_Zscore_L <- NA
+  InteractionScores$Raw_Shift_K <- NA
+  InteractionScores$Z_Shift_K <- NA
+  InteractionScores$lm_Score_K <- NA
+  InteractionScores$Z_lm_K <- NA
+  InteractionScores$R_Squared_K <- NA
+  InteractionScores$Sum_Z_Score_K <- NA
+  InteractionScores$Avg_Zscore_K <- NA
+  InteractionScores$Raw_Shift_r <- NA
+  InteractionScores$Z_Shift_r <- NA
+  InteractionScores$lm_Score_r <- NA
+  InteractionScores$Z_lm_r <- NA
+  InteractionScores$R_Squared_r <- NA
+  InteractionScores$Sum_Z_Score_r <- NA
+  InteractionScores$Avg_Zscore_r <- NA
+  InteractionScores$Raw_Shift_AUC <- NA
+  InteractionScores$Z_Shift_AUC <- NA
+  InteractionScores$lm_Score_AUC <- NA
+  InteractionScores$Z_lm_AUC <- NA
+  InteractionScores$R_Squared_AUC <- NA
+  InteractionScores$Sum_Z_Score_AUC <- NA
+  InteractionScores$Avg_Zscore_AUC <- NA
+  InteractionScores$NG <- NA
+  InteractionScores$DB <- NA
+  InteractionScores$SM <- NA
+  
+  for(i in 1:num_genes){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_L-lm_mean_L)/lm_sd_L
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_K-lm_mean_K)/lm_sd_K
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_r-lm_mean_r)/lm_sd_r
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_AUC-lm_mean_AUC)/lm_sd_AUC
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-NA
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction)
+    }
+    
+    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass Int Calculation loop")
+  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L,decreasing=TRUE),]
+  InteractionScores <- InteractionScores[order(InteractionScores$NG,decreasing=TRUE),]
+  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
+  #X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG,decreasing=TRUE),]
+  write.csv(InteractionScores,paste(outputpath,"ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_K <- InteractionScores[InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores_deletion_enhancers_L[!is.na(InteractionScores_deletion_enhancers_L$OrfRep),]
+  InteractionScores_deletion_enhancers_K <- InteractionScores_deletion_enhancers_K[!is.na(InteractionScores_deletion_enhancers_K$OrfRep),]
+  InteractionScores_deletion_suppressors_L <- InteractionScores_deletion_suppressors_L[!is.na(InteractionScores_deletion_suppressors_L$OrfRep),]
+  InteractionScores_deletion_suppressors_K <- InteractionScores_deletion_suppressors_K[!is.na(InteractionScores_deletion_suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores_deletion_enhancers_and_Suppressors_L[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores_deletion_enhancers_and_Suppressors_K[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE)
+  
+  #get enhancers and suppressors for linear regression
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2,]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores[InteractionScores$Z_lm_K <= -2,]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores_deletion_enhancers_L_lm[!is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores_deletion_enhancers_K_lm[!is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores_deletion_suppressors_L_lm[!is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores_deletion_suppressors_K_lm[!is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores_deletion_enhancers_and_Suppressors_L_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores_deletion_enhancers_and_Suppressors_K_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE)
+  
+  
+  write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+  print('ln 1570 write StudyInfo.csv after ')
+        #write.table(Labels,file=paste("../Code/StudyInfo.txt"),sep="\t",row.names = FALSE)
+  
+  for(i in 1:num_genes){
+    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel,]
+    
+    p_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_K,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final,X_ZCalculations)
+    }
+  }
+  print("Pass Int ggplot loop")
+  write.csv(X_stats_interaction_ALL_final,paste(outputpath,"ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  
+  
+  
+  
+  Blank <- ggplot(X2_RF) + geom_blank()
+  
+  pdf(paste(outputpath,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_l[[num]],p_K[[num]],p_r[[num]],p_AUC[[num]],p_l[[num+1]],p_K[[num+1]],p_r[[num+1]],p_AUC[[num+1]],p_l[[num+2]],p_K[[num+2]],p_r[[num+2]],p_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_l[[364]],p_K[[364]],p_r[[364]],p_l[[365]],p_K[[365]],p_r[[365]],p_l[[366]],p_K[[366]],p_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_K[[num+3]],p_r[[num+3]],p1[[num+4]],p_K[[num+4]],p_r[[num+4]]
+  }
+  if(num_genes != (num+2)){
+    total_num = num_genes - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],p_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  
+  
+  pdf(paste(outputpath,"RF_InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(12000,0)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes_RF)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes_RF)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_rf_l[[num]],p_rf_K[[num]],p_rf_r[[num]],p_rf_AUC[[num]],p_rf_l[[num+1]],p_rf_K[[num+1]],p_rf_r[[num+1]],p_rf_AUC[[num+1]],p_rf_l[[num+2]],p_rf_K[[num+2]],p_rf_r[[num+2]],p_rf_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_rf_l[[364]],p_rf_K[[364]],p_rf_r[[364]],p_rf_l[[365]],p_rf_K[[365]],p_rf_r[[365]],p_rf_l[[366]],p_rf_K[[366]],p_rf_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p1[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]]
+  }
+  if(num_genes_RF != (num+2)){
+    total_num = num_genes_RF - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],p_rf_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  #print rank plots for L and K gene interactions
+  
+  
+  InteractionScores_AdjustMissing <- InteractionScores
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L),]$Avg_Zscore_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K),]$Avg_Zscore_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r),]$Avg_Zscore_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC),]$Avg_Zscore_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank <- NA
+  InteractionScores_AdjustMissing$K_Rank <- NA
+  InteractionScores_AdjustMissing$r_Rank <- NA
+  InteractionScores_AdjustMissing$AUC_Rank <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
+  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
+  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
+  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
+  
+  #
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L),]$Z_lm_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K),]$Z_lm_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r),]$Z_lm_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC),]$Z_lm_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- NA
+  InteractionScores_AdjustMissing$K_Rank_lm <- NA
+  InteractionScores_AdjustMissing$r_Rank_lm <- NA
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
+  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
+  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
+  
+  
+  
+  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  
+  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,]
+  
+  #find overlaps
+  X_NArm$Overlap <- "No Effect"
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Both")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Both")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2,]$Overlap <- "Deletion Enhancer lm only")
+  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2,]$Overlap <- "Deletion Suppressor lm only")
+  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L <- lm(X_NArm$Z_lm_L~X_NArm$Avg_Zscore_L)
+  L_lm <- summary(get_lm_L)
+  
+  get_lm_K <- lm(X_NArm$Z_lm_K~X_NArm$Avg_Zscore_K)
+  K_lm <- summary(get_lm_K)
+  
+  get_lm_r <- lm(X_NArm$Z_lm_r~X_NArm$Avg_Zscore_r)
+  r_lm <- summary(get_lm_r)
+  
+  get_lm_AUC <- lm(X_NArm$Z_lm_AUC~X_NArm$Avg_Zscore_AUC)
+  AUC_lm <- summary(get_lm_AUC)
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm L") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_K,Z_lm_K)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm K") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(K_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_r,Z_lm_r)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm r") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(r_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_AUC,Z_lm_AUC)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm AUC") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(AUC_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  dev.off()
+  
+  
+  lm_v_Zscore_L <- ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L,ORF=OrfRep,Gene=Gene,NG=NG,SM=SM,DB=DB)) + geom_point(aes(color=Overlap),shape=3) + 
+    geom_smooth(method = "lm",color=1) +  ggtitle("Avg Zscore vs lm L") +
+    geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+    annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right()
+  
+  pgg <- ggplotly(lm_v_Zscore_L)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath,"Avg_Zscore_vs_lm_NA_rm.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
+  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
+  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
+  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
+  
+  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
+  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
+  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
+  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L2 <- lm(X_NArm$L_Rank_lm~X_NArm$L_Rank)
+  L_lm2 <- summary(get_lm_L2)
+  
+  get_lm_K2 <- lm(X_NArm$K_Rank_lm~X_NArm$K_Rank)
+  K_lm2 <- summary(get_lm_K2)
+  
+  get_lm_r2 <- lm(X_NArm$r_Rank_lm~X_NArm$r_Rank)
+  r_lm2 <- summary(get_lm_r2)
+  
+  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm~X_NArm$AUC_Rank)
+  AUC_lm2 <- summary(get_lm_AUC2)
+  
+  num_genes_NArm2 <- (dim(X_NArm)[1])/2
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_ranked_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm L") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(K_Rank,K_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm K") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(K_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(r_Rank,r_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm r") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(r_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(AUC_Rank,AUC_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank of Avg Zscore vs lm AUC") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(AUC_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  
+  
+  dev.off()
+  
+  
+  
+  Rank_L_1SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+}
+
+
+
+#get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+get_lm_1 <- lm(X_NArm$Z_lm_K~X_NArm$Z_lm_L)
+L_lm_1 <- summary(get_lm_1)
+
+get_lm_2 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_L)
+L_lm_2 <- summary(get_lm_2)
+
+get_lm_3 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_L)
+L_lm_3 <- summary(get_lm_3)
+
+get_lm_4 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_K)
+L_lm_4 <- summary(get_lm_4)
+
+get_lm_5 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_K)
+L_lm_5 <- summary(get_lm_5)
+
+get_lm_6 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_r)
+L_lm_6 <- summary(get_lm_6)
+
+
+pdf(file=paste(outputpath,"Correlation_CPPs.pdf",sep=""),width = 10, height = 7, onefile = TRUE)
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L),]
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_K),color="cyan") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_r),color="cyan") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_r),color="cyan") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_r,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+
+
+
+
+dev.off()
+
+
+#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE)
+timestamp()
+
+#BoneYard***********************************************
+#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced.
+#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. 
+#Corruptable but better than nothing.
+#if(is.na(Args[2])){
+# std=3
+#}else {
+# std= Arg[2]
+#Delta_Background_sdFactor <- 2  #Args[3]
+#DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#} 
+
diff --git a/workflow/.old/templates/qhtcp/Exp3/.DS_Store b/workflow/.old/templates/qhtcp/Exp3/.DS_Store
new file mode 100644
index 00000000..ccdc65b6
Binary files /dev/null and b/workflow/.old/templates/qhtcp/Exp3/.DS_Store differ
diff --git a/workflow/.old/templates/qhtcp/Exp3/.Rhistory b/workflow/.old/templates/qhtcp/Exp3/.Rhistory
new file mode 100644
index 00000000..e69de29b
diff --git a/workflow/.old/templates/qhtcp/Exp3/ExpFrontend.m b/workflow/.old/templates/qhtcp/Exp3/ExpFrontend.m
new file mode 100644
index 00000000..2bdc98c3
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp3/ExpFrontend.m
@@ -0,0 +1,53 @@
+%FrontEnd utility to copy source result sheet into Exp_ folders of
+%StudiesQHTCP/StudyName/Exp1(2,3,4). This allow the automation of path
+%capture to the StudiesDataArchieve.txt study log.
+%Select, copy and Capture Study Exp_ details to study log
+
+%Exp meta data collection 
+W=pwd;
+%Load matlab stored data file into workspace
+try 
+ExpLabel= strcat('Exp',W(end))
+questdlg('\fontsize{20} Select the !!Results File','File Selection','OK', struct('Default','OK','Interpreter','tex'));
+[resFile,resPath]= uigetfile('*.txt')
+copyfile((fullfile(resPath,resFile)),fullfile(W))
+resDate= char(regexp(resFile, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)','match'))
+cd ..
+Wstudy= pwd
+studyDate= datetime("today");
+S.sDate(1) = {studyDate};
+if ispc
+    lastSep=max(strfind(Wstudy,'\'))
+    studyName= Wstudy((lastSep+1):end)
+else
+    lastSep=max(strfind(Wstudy,'/'))
+    studyName= Wstudy((lastSep+1):end)
+end
+
+S.sDate(1)= {studyDate};
+S.sName(1)= {studyName}
+S.sPath(1)= {Wstudy}
+S.ELabel(1)= {ExpLabel}
+S.EresDate(1)= {resDate}
+S.EresFile(1)= {resFile}
+S.EresPath(1)= {resPath}
+   
+cd ..
+
+fid = fopen('StudiesDataArchive.txt','a');  
+fprintf(fid,'StudyDate\tStudyName\tStudyPath\tExpNum\tExpDate\tExpPath\tResultFile\n');
+fprintf(fid, '%s\t %s\t %s\t %s\t %s\t %s\t %s \n',S.sDate{1},S.sName{1},S.sPath{1},S.ELabel{1},S.EresDate{1},S.EresPath{1},S.EresFile{1});
+fclose(fid);
+%save((fullfile(pwd,'studyLog.mat')), 'S')  
+fclose('all');
+
+catch
+    cd(W)
+    disp('Error: Unable to Execute ExpFrontend.m')
+end
+
+
+
+
+
+
diff --git a/workflow/.old/templates/qhtcp/Exp3/NotesExp3 b/workflow/.old/templates/qhtcp/Exp3/NotesExp3
new file mode 100644
index 00000000..e69de29b
diff --git a/workflow/.old/templates/qhtcp/Exp3/Z_InteractionTemplate.R b/workflow/.old/templates/qhtcp/Exp3/Z_InteractionTemplate.R
new file mode 100644
index 00000000..fe31ca14
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp3/Z_InteractionTemplate.R
@@ -0,0 +1,2730 @@
+#Based on InteractionTemplate.R which is based on Sean Santose's Interaction_V5 script.
+#Adapt SS For Structured Data storage but using command line scripts
+###Set up the required libraries, call required plot theme elements and set up the command line arguments
+library("ggplot2")
+library("plyr")
+library("extrafont")
+library("gridExtra")
+library("gplots")
+library("RColorBrewer")
+library("stringr")
+#library("gdata")
+library(plotly)
+library(htmlwidgets)
+
+Args <- commandArgs(TRUE)
+input_file <- Args[1] #"!!Results_17_0827_yor1null-rpl12anull misLabeledAsFrom MI 17_0919_yor1-curated.txt"  #Args[1]  #Arg 1 #"!!ResultsStd_JS 19_1224_HLEG_P53.txt" is the !!results ... .txt
+#Tool to find a file and copy it to desired location
+destDir= getwd()
+#srcFile= file.choose()
+#file.copy(srcFile, destDir)
+#input_file= tail(strsplit(srcFile,"[/]")[[1]],1)
+
+
+
+#Path to Output Directory
+#W=getwd() #R is F'd up, Can't use, Any legitamate platform could build out dirs from this
+outDir <- "ZScores/" #"Args[2] #paste0(W,"/ZScores/")
+subDir <- outDir     #Args[2]
+
+if (file.exists(subDir)){
+  outputpath <- subDir
+} else {
+  dir.create(file.path(subDir))
+}
+
+if (file.exists(paste(subDir,"QC/",sep=""))){
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+} else {
+  dir.create(file.path(paste(subDir,"QC/",sep="")))
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+}
+#define the output path (formerly the second argument from Rscript)
+outputpath <- outDir 
+
+#Set Args[2] the Background contamination noise filter as a function of standard deviation
+#std= as.numeric(Args[2])
+#Sean recommends 3 or 5 SD factor.
+#Capture Exp_ number,use it to Save Args[2]{std}to Labels field and then Write to Labels to studyInfo.txt for future reference
+Labels <- read.csv(file= "../Code/StudyInfo.csv",stringsAsFactors = FALSE)  #,sep= ",")
+print("Be sure to enter Background noise filter standard deviation i.e., 3 or 5 per Sean")
+
+#User prompt for std multiplier Value
+cat("Enter a Standard Deviation value to noise filter \n")
+inpChar<- readLines(file("stdin"), n = 1L)
+cat(paste("Standard Deviation Value is", inpChar, "\n"))
+inpNum= as.numeric(inpChar)
+#set std deviation multiplier default if no user entry
+if(!is.na(inpNum)){
+  std= inpNum
+}else{std= 3}
+
+
+expNumber<- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd()))
+Labels[expNumber,3]= as.numeric(std)
+Delta_Background_sdFactor <- std
+DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#Write Background SD value to studyInfo.txt file
+#write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+print('ln 50 write StudyInfo.csv ')
+#write.table(Labels,file=paste(outputpath,"StudyInfo.txt"),sep = "\t",row.names = FALSE)
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++BEGIN USER DATA SELECTION SECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+
+#read in the data
+X <- read.delim(input_file,skip=2,as.is=T,row.names=1,strip.white=TRUE)
+X <- X[!(X[[1]]%in%c("","Scan")),]
+#X <- X[!(X[[1]]%in%c(61:76)),]  #Remove dAmp plates which are Scans 61 thru 76
+
+#X <- X[which(X$Specifics == "WT"),]
+
+#X_length <- length(X[1,])
+#X_end <- length(X[1,]) - 2
+#X <- X[,c(1:42,X_end:X_length)]
+
+
+#use numeric data to perform operations
+X$Col <- as.numeric(X$Col)
+X$Row <- as.numeric(X$Row)
+X$l <- as.numeric(X$l)
+X$K <- as.numeric(X$K)
+X$r <- as.numeric(X$r)
+X$Scan <- as.numeric(X$Scan)
+X$AUC <- as.numeric(X$AUC)
+X$LstBackgrd <- as.numeric(X$LstBackgrd)
+X$X1stBackgrd <- as.numeric(X$X1stBackgrd)
+
+#Sometimes the an experimenter may have placed the non-varying drug in the 'Drug' col instead of the 'Modifier1' col
+#as was the case in Gemcitabin and Cytarabin experiments.
+#The following allows user to rename columns so as to get the appropriate
+#data where it needs to be for the script to run properly.
+#colnames(X)[7]  <- "Modifier1"
+#colnames(X)[8]  <- "Conc1"
+#colnames(X)[10]  <- "Drug"
+#colnames(X)[11]  <- "Conc"
+
+#set the OrfRep to YDL227C for the ref data
+X[X$ORF == "YDL227C",]$OrfRep <- "YDL227C"
+#Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. 
+#That is the first DM plates are removed from the data set with the following.
+#X <- X[X$Conc1 != "0ug/ml",] #This removes data with dox ==0 leaving gene expression on with four different concentrations of Gemcytabin
+X <- X[X$Drug != "BMH21",] #This removes data concerning BMH21 for this experiment
+
+#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. 
+#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used.
+#try(X <- X[X$ORF != "bad_spot",])
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=
+
+#get total number of drug concentrations
+Total_Conc_Nums <- length(unique(X$Conc))
+
+#function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
+numextract <- function(string){ 
+  str_extract(string, "\\-*\\d+\\.*\\d*")
+} 
+
+#generate a new column with the numeric drug concs
+X$Conc_Num <- as.numeric(numextract(X$Conc))
+#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
+X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1
+
+#Get the max factor for concentration
+MAX_CONC <- max(X$Conc_Num_Factor)
+#if treating numbers not as factors uncomment next line and comment out previous line
+#MAX_CONC <- max(X$Conc_Num)
+
+#remove wells with problems for making graphs and to not include in summary statistics
+X <- X[X$Gene != "BLANK",]
+X <- X[X$Gene != "Blank",] 
+X <- X[X$ORF != "Blank",]
+X <- X[X$Gene != "blank",]
+#X <- X[X$Gene != "HO",]
+Xbu= X
+#Inserted to use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script). This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
+#Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to write over the RF1&RF2 geneNames which caused a variance with his code results 
+#in the Z_lm_L,K,r&AUC output values. Values correlated well but were off by a multiplier factor.
+SGDgeneList= "../Code/SGD_features.tab"
+genes = data.frame(read.delim(file=SGDgeneList,quote="",header=FALSE,colClasses = c(rep("NULL",3), rep("character", 2), rep("NULL", 11))))
+for(i in 1:length(X[,14])){
+  ii= as.numeric(i)
+  line_num = match(X[ii,14],genes[,1],nomatch=1)
+  OrfRepColNum= as.numeric(match('OrfRep',names(X)))
+  if(X[ii,OrfRepColNum]!= "YDL227C"){
+     X[ii,15] = genes[line_num,2]
+  }
+  if((X[ii,15] == "")||(X[ii,15] == "OCT1")){
+    X[ii,15] = X[ii,OrfRepColNum]
+  }
+}
+Xblankreplace= X
+#X= Xbu #for restore testing restore X if geneName 'Match' routine needs changing
+
+#Remove dAmPs *******************************
+#jlh confirmed to leave dAmps in so comment out this section
+#DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
+#Damps <- read.delim(DAmPs_List,header=F)
+
+#X <- X[!(X$ORF %in% Damps$V1),]  #fix this to Damps[,1]
+#XafterDampsRM=X  #Backup for debugging especially when Rstudio goes crazy out of control
+# ***********
+
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++END USER DATA SELECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+print("ln137 End of User Section including blank gene writeOver")
+#++++Begin Graphics Boiler Plate Section+++++++++++++++++++++++++++++++++++++++
+#theme elements for plots
+theme_Publication <- function(base_size=14, base_family="sans") {
+  library(grid)
+  library(ggthemes)
+  (theme_foundation(base_size=base_size, base_family=base_family)
+    + theme(plot.title = element_text(face = "bold",
+                                      size = rel(1.2), hjust = 0.5),
+            text = element_text(),
+            panel.background = element_rect(colour = NA),
+            plot.background = element_rect(colour = NA),
+            panel.border = element_rect(colour = NA),
+            axis.title = element_text(face = "bold",size = rel(1)),
+            axis.title.y = element_text(angle=90,vjust =2),
+            axis.title.x = element_text(vjust = -0.2),
+            axis.text = element_text(), 
+            axis.line = element_line(colour="black"),
+            axis.ticks = element_line(),
+            panel.grid.major = element_line(colour="#f0f0f0"),
+            panel.grid.minor = element_blank(),
+            legend.key = element_rect(colour = NA),
+            legend.position = "bottom",
+            legend.direction = "horizontal",
+            legend.key.size= unit(0.2, "cm"),
+            legend.spacing = unit(0, "cm"),
+            legend.title = element_text(face="italic"),
+            plot.margin=unit(c(10,5,5,5),"mm"),
+            strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+            strip.text = element_text(face="bold")
+    ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  library(scales)
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
+  (theme_foundation(base_size=base_size, base_family=base_family)
+   + theme(plot.title = element_text(face = "bold",
+                                     size = rel(1.2), hjust = 0.5),
+           text = element_text(),
+           panel.background = element_rect(colour = NA),
+           plot.background = element_rect(colour = NA),
+           panel.border = element_rect(colour = NA),
+           axis.title = element_text(face = "bold",size = rel(1)),
+           axis.title.y = element_text(angle=90,vjust =2),
+           axis.title.x = element_text(vjust = -0.2),
+           axis.text = element_text(), 
+           axis.line = element_line(colour="black"),
+           axis.ticks = element_line(),
+           panel.grid.major = element_line(colour="#f0f0f0"),
+           panel.grid.minor = element_blank(),
+           legend.key = element_rect(colour = NA),
+           legend.position = "right",
+           legend.direction = "vertical",
+           legend.key.size= unit(0.5, "cm"),
+           legend.spacing = unit(0, "cm"),
+           legend.title = element_text(face="italic"),
+           plot.margin=unit(c(10,5,5,5),"mm"),
+           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+           strip.text = element_text(face="bold")
+   ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+#print timestamp for initial time the code starts
+timestamp()
+#+++++BEGIN QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+###Part 2 - Quality control
+#print quality control graphs for each dataset before removing data due to contamination
+#and before adjusting missing data to max theoretical values
+
+#plate analysis plot
+#plate analysis is a quality check to identify plate effects containing anomalies
+
+Plate_Analysis_L <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+
+
+Plate_Analysis_L_Box <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K_Box <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r_Box <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+#quality control - values with a high delta background likely have heavy contamination
+#check the frequency of these values
+#report the L and K values of these spots
+#report the number to be removed based on the Delta_Background_Tolerance
+X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd
+
+
+#raw l vs K before QC
+Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle("Raw L vs K before QC") +
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8)
+Raw_l_vs_K_beforeQC
+dev.off()
+pgg <- ggplotly(Raw_l_vs_K_beforeQC)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_beforeQC.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+
+#set delta background tolerance based on 3 sds from the mean delta background
+Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(DelBGFactr*sd(X$Delta_Backgrd))
+#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
+print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" "))
+
+Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+
+
+X_Delta_Backgrd_above_Tolerance <- X[X$Delta_Backgrd >= Delta_Background_Tolerance,]
+
+X_Delta_Backgrd_above_Tolerance_K_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$K,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_L_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$l,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_toRemove <- dim(X_Delta_Backgrd_above_Tolerance)[1]
+
+X_Delta_Backgrd_above_Tolerance_L_vs_K <- ggplot(X_Delta_Backgrd_above_Tolerance,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle(paste("Raw L vs K for strains above delta background threshold of",Delta_Background_Tolerance,"or above")) +
+  annotate("text",x=X_Delta_Backgrd_above_Tolerance_L_halfmedian,y=X_Delta_Backgrd_above_Tolerance_K_halfmedian,
+           label = paste("Strains above delta background tolerance = ",X_Delta_Backgrd_above_Tolerance_toRemove)) + 
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8)
+X_Delta_Backgrd_above_Tolerance_L_vs_K
+dev.off()
+pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+#frequency plot for all data vs. the delta_background
+DeltaBackground_Frequency_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_density() +
+  ggtitle("Density plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+#bar plot for all data vs. the delta_background
+DeltaBackground_Bar_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_bar() +
+  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+pdf(file = paste(outputpath_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8)
+print(DeltaBackground_Frequency_Plot)
+print(DeltaBackground_Bar_Plot)
+dev.off()
+
+
+#Need to identify missing data, and differentiate between this data and removed data so the removed data can get set to NA and the missing data can get set to max theoretical values
+#1 for missing data, 0 for non missing data
+#Use "NG" for NoGrowth rather than "missing"
+X$NG <- 0
+try(X[X$l == 0 & !is.na(X$l),]$NG <- 1)
+
+#1 for removed data, 0 non removed data
+#Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
+X$DB <- 0
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$DB <- 1)
+
+#replace the CPPs for l, r, AUC and K (must be last!) for removed data
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$l <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$r <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$AUC <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$K <- NA)
+
+
+Plate_Analysis_L_afterQC <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L
+Plate_Analysis_L_afterQC
+Plate_Analysis_K
+Plate_Analysis_K_afterQC
+Plate_Analysis_r
+Plate_Analysis_r_afterQC
+Plate_Analysis_AUC
+Plate_Analysis_AUC_afterQC
+Plate_Analysis_Delta_Backgrd
+Plate_Analysis_Delta_Backgrd_afterQC
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box
+Plate_Analysis_L_Box_afterQC
+Plate_Analysis_K_Box
+Plate_Analysis_K_Box_afterQC
+Plate_Analysis_r_Box
+Plate_Analysis_r_Box_afterQC
+Plate_Analysis_AUC_Box
+Plate_Analysis_AUC_Box_afterQC
+Plate_Analysis_Delta_Backgrd_Box
+Plate_Analysis_Delta_Backgrd_Box_afterQC
+dev.off()
+
+#remove the zero values and print plate analysis
+X_noZero <- X[which(X$l > 0),]
+Plate_Analysis_L_afterQC_Z <- ggplot(X_noZero,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC_Z <- ggplot(X_noZero,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC_Z <- ggplot(X_noZero,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC_Z <- ggplot(X_noZero,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC_Z <- ggplot(X_noZero,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L_afterQC_Z
+Plate_Analysis_K_afterQC_Z
+Plate_Analysis_r_afterQC_Z
+Plate_Analysis_AUC_afterQC_Z
+Plate_Analysis_Delta_Backgrd_afterQC_Z
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box_afterQC_Z
+Plate_Analysis_K_Box_afterQC_Z
+Plate_Analysis_r_Box_afterQC_Z
+Plate_Analysis_AUC_Box_afterQC_Z
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
+dev.off()
+
+#remove dataset with zeros removed
+rm(X_noZero)
+
+
+#X_test_missing_and_removed <- X[X$Removed == 1,]
+
+#calculate summary statistics for all strains, including both background and the deletions
+X_stats_ALL <- ddply(X, c("Conc_Num","Conc_Num_Factor"), summarise,
+                     N = (length(l)),
+                     mean_L = mean(l,na.rm=TRUE),
+                     median_L = median(l,na.rm=TRUE),
+                     max_L = max(l,na.rm=TRUE),
+                     min_L = min(l,na.rm=TRUE),
+                     sd_L = sd(l,na.rm=TRUE),
+                     se_L = sd_L / sqrt(N-1),
+                     mean_K = mean(K,na.rm=TRUE),
+                     median_K = median(K,na.rm=TRUE),
+                     max_K = max(K,na.rm=TRUE),
+                     min_K = min(K,na.rm=TRUE),
+                     sd_K = sd(K,na.rm=TRUE),
+                     se_K = sd_K / sqrt(N-1),
+                     mean_r = mean(r,na.rm=TRUE),
+                     median_r = median(r,na.rm=TRUE),
+                     max_r = max(r,na.rm=TRUE),
+                     min_r = min(r,na.rm=TRUE),
+                     sd_r = sd(r,na.rm=TRUE),
+                     se_r = sd_r / sqrt(N-1),
+                     mean_AUC = mean(AUC,na.rm=TRUE),
+                     median_AUC = median(AUC,na.rm=TRUE),
+                     max_AUC = max(AUC,na.rm=TRUE),
+                     min_AUC = min(AUC,na.rm=TRUE),
+                     sd_AUC = sd(AUC,na.rm=TRUE),
+                     se_AUC = sd_AUC / sqrt(N-1)
+)
+#print(X_stats_ALL_L)
+
+write.csv(X_stats_ALL,file=paste(outputpath,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE)
+#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+##### Part 3 - Generate summary statistics and calculate the max theoretical L value
+##### Calculate the Z score at each drug conc for each deletion strain
+
+
+#get the background strains - can be modified to take another argument but for most screens will just be YDL227C
+Background_Strains <- c("YDL227C")
+
+#first part of loop will go through for each background strain 
+#most cases there will only be one YDL227C
+for(s in Background_Strains){
+  X_Background <- X[X$OrfRep == s,]
+  
+  #if there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
+  #we may want to consider in some cases giving the max high value to L depending on the data type
+  if(table(X_Background$l)[1] == 0){
+    X_Background[X_Background$l == 0,]$l <- NA
+    X_Background[X_Background$K == 0,]$K <- NA
+    X_Background[X_Background$r == 0,]$r <- NA
+    X_Background[X_Background$AUC == 0,]$AUC <- NA
+  }
+  
+  X_Background <- X_Background[!is.na(X_Background$l),]
+  
+  #get summary stats for L, K, R, AUC
+  X_stats_BY_L <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(l)),
+                        mean = mean(l,na.rm=TRUE),
+                        median = median(l,na.rm=TRUE),
+                        max = max(l,na.rm=TRUE),
+                        min = min(l,na.rm=TRUE),
+                        sd = sd(l,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L)
+  X1_SD <- max(X_stats_BY_L$sd)
+  
+  X_stats_BY_K <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(K)),
+                        mean = mean(K,na.rm=TRUE),
+                        median = median(K,na.rm=TRUE),
+                        max = max(K,na.rm=TRUE),
+                        min = min(K,na.rm=TRUE),
+                        sd = sd(K,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_K <- max(X_stats_BY_K$sd)
+  
+  
+  X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = length(r),
+                        mean = mean(r,na.rm=TRUE),
+                        median = median(r,na.rm=TRUE),
+                        max = max(r,na.rm=TRUE),
+                        min = min(r,na.rm=TRUE),
+                        sd = sd(r,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_r <- max(X_stats_BY_r$sd)
+  
+  X_stats_BY_AUC <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                          N = length(AUC),
+                          mean = mean(AUC,na.rm=TRUE),
+                          median = median(AUC,na.rm=TRUE),
+                          max = max(AUC,na.rm=TRUE),
+                          min = min(AUC,na.rm=TRUE),
+                          sd = sd(AUC,na.rm=TRUE),
+                          se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
+  
+  X_stats_BY <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                      N = (length(l)),
+                      mean_L = mean(l,na.rm=TRUE),
+                      median_L = median(l,na.rm=TRUE),
+                      max_L = max(l,na.rm=TRUE),
+                      min_L = min(l,na.rm=TRUE),
+                      sd_L = sd(l,na.rm=TRUE),
+                      se_L = sd_L / sqrt(N-1),
+                      mean_K = mean(K,na.rm=TRUE),
+                      median_K = median(K,na.rm=TRUE),
+                      max_K = max(K,na.rm=TRUE),
+                      min_K = min(K,na.rm=TRUE),
+                      sd_K = sd(K,na.rm=TRUE),
+                      se_K = sd_K / sqrt(N-1),
+                      mean_r = mean(r,na.rm=TRUE),
+                      median_r = median(r,na.rm=TRUE),
+                      max_r = max(r,na.rm=TRUE),
+                      min_r = min(r,na.rm=TRUE),
+                      sd_r = sd(r,na.rm=TRUE),
+                      se_r = sd_r / sqrt(N-1),
+                      mean_AUC = mean(AUC,na.rm=TRUE),
+                      median_AUC = median(AUC,na.rm=TRUE),
+                      max_AUC = max(AUC,na.rm=TRUE),
+                      min_L = min(AUC,na.rm=TRUE),
+                      sd_AUC = sd(AUC,na.rm=TRUE),
+                      se_AUC = sd_AUC / sqrt(N-1)
+  )
+  
+  write.csv(X_stats_BY,file=paste(outputpath,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE)
+  
+  #calculate the max theoretical L values
+  #only look for max values when K is within 2SD of the ref strain
+  for(q in unique(X$Conc_Num_Factor)){
+    if(q == 0){
+      X_within_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[!is.na(X_within_2SD_K$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) ,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+      X_outside_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[!is.na(X_outside_2SD_K$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1]))  ,]
+      #X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+    }
+    if(q > 0){
+      X_within_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K <- rbind(X_within_2SD_K,X_within_2SD_K_temp)
+      
+      X_outside_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      #X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      X_outside_2SD_K <- rbind(X_outside_2SD_K,X_outside_2SD_K_temp)
+    }
+  }
+  
+  X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                     N = (length(l)),
+                                     mean = mean(l),
+                                     median = median(l),
+                                     max = max(l,na.rm=TRUE),
+                                     min = min(l,na.rm=TRUE),
+                                     sd = sd(l),
+                                     se = sd / sqrt(N-1),
+                                     z_max = (max-mean)/sd
+  )
+  print(X_stats_BY_L_within_2SD_K)
+  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)
+  write.csv(X_stats_BY_L_within_2SD_K,file=paste(outputpath_QC,"Max_Observed_L_Vals_for_spots_within_2SD_K.csv",sep=""),row.names=FALSE)
+  
+  X_stats_BY_L_outside_2SD_K <- ddply(X_outside_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                      N = (length(l)),
+                                      mean = mean(l),
+                                      median = median(l),
+                                      max = max(l,na.rm=TRUE),
+                                      min = min(l,na.rm=TRUE),
+                                      sd = sd(l),
+                                      se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L_outside_2SD_K)
+  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)
+  
+  #X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l,]
+  Outside_2SD_K_L_vs_K <- ggplot(X_outside_2SD_K,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  print(Outside_2SD_K_L_vs_K)
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"RawL_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  Outside_2SD_K_delta_background_vs_K <- ggplot(X_outside_2SD_K,aes(Delta_Backgrd,K,color=as.factor(Conc_Num))) + geom_point(aes(l=l,ORF=ORF,Gene=Gene),shape=3,position="jitter") +
+    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  Outside_2SD_K_delta_background_vs_K
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"DeltaBackground_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  #get the background strain mean values at the no drug conc to calculate shift
+  Background_L <- X_stats_BY_L$mean[1]
+  Background_K <- X_stats_BY_K$mean[1]
+  Background_r <- X_stats_BY_r$mean[1]
+  Background_AUC <- X_stats_BY_AUC$mean[1]
+  
+  #create empty plots for plotting element
+  p_l <-  ggplot()
+  p_K <-  ggplot()
+  p_r <-  ggplot()
+  p_AUC <-  ggplot()
+  
+  p_rf_l <-  ggplot()
+  p_rf_K <-  ggplot()
+  p_rf_r <-  ggplot()
+  p_rf_AUC <-  ggplot()
+  
+  #get only the deletion strains
+  X2 <- X
+  X2 <- X2[X2$OrfRep != "YDL227C",]
+  
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2$Conc_Num))){
+    Concentration <- unique(X2$Conc_Num)[i]
+    X2_temp <- X2[X2$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_new <- X2_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      
+      #X2_temp[X2_temp$K == 0,]$K <- X_stats_ALL_K$max[i]
+      #X2_temp[X2_temp$r == 0,]$r <- X_stats_ALL_r$max[i]
+      #X2_temp[X2_temp$AUC == 0,]$AUC <- X_stats_ALL_AUC$max[i]
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+      
+      X2_new <- rbind(X2_new,X2_temp)
+      
+    }
+  }
+  X2 <- X2_new
+  
+  
+  #get only the RF strains
+  X2_RF <- X
+  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",]
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2_RF$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2_RF$Conc_Num))){
+    Concentration <- unique(X2_RF$Conc_Num)[i]
+    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_RF_new <- X2_RF_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      print(paste("Check loop order, if error, refs have no L values outside theoretical max L, for REFs, conc =",Concentration,sep=" "))
+      
+      X2_RF_new <- rbind(X2_RF_new,X2_RF_temp)
+      
+    }
+  }
+  X2_RF <- X2_RF_new
+  
+  
+  #######Part 4 Get the RF Z score values
+  #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
+  X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_")
+  
+  num_genes_RF <- length(unique(X2_RF$OrfRep))
+  print(num_genes_RF)
+  
+  
+  #create the output data.frame containing columns for each RF strain
+  InteractionScores_RF <- unique(X2_RF["OrfRep"])
+  #InteractionScores_RF$Gene <- unique(X2$Gene)
+  InteractionScores_RF$Gene <- NA
+  InteractionScores_RF$Raw_Shift_L <- NA
+  InteractionScores_RF$Z_Shift_L <- NA
+  InteractionScores_RF$lm_Score_L <- NA
+  InteractionScores_RF$Z_lm_L <- NA  
+  InteractionScores_RF$R_Squared_L <- NA
+  InteractionScores_RF$Sum_Z_Score_L <- NA
+  InteractionScores_RF$Avg_Zscore_L <- NA
+  InteractionScores_RF$Raw_Shift_K <- NA
+  InteractionScores_RF$Z_Shift_K <- NA
+  InteractionScores_RF$lm_Score_K <- NA
+  InteractionScores_RF$Z_lm_K <- NA  
+  InteractionScores_RF$R_Squared_K <- NA
+  InteractionScores_RF$Sum_Z_Score_K <- NA
+  InteractionScores_RF$Avg_Zscore_K <- NA
+  InteractionScores_RF$Raw_Shift_r <- NA
+  InteractionScores_RF$Z_Shift_r <- NA
+  InteractionScores_RF$lm_Score_r <- NA
+  InteractionScores_RF$Z_lm_r <- NA
+  InteractionScores_RF$R_Squared_r <- NA
+  InteractionScores_RF$Sum_Z_Score_r <- NA
+  InteractionScores_RF$Avg_Zscore_r <- NA
+  InteractionScores_RF$Raw_Shift_AUC <- NA
+  InteractionScores_RF$Z_Shift_AUC <- NA
+  InteractionScores_RF$lm_Score_AUC <- NA
+  InteractionScores_RF$Z_lm_AUC <- NA
+  InteractionScores_RF$R_Squared_AUC <- NA
+  InteractionScores_RF$Sum_Z_Score_AUC <- NA
+  InteractionScores_RF$Avg_Zscore_AUC <- NA
+  InteractionScores_RF$NG <- NA
+  InteractionScores_RF$SM <- NA
+  
+  
+  for(i in 1:num_genes_RF){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2_RF$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      gene_lm_AUC <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      gene_interaction_AUC <- NA
+      r_squared_AUC <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction)
+    }
+    
+    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass RF Calculation loop")
+  
+  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  print(paste("Mean RF linear regression score L",lm_mean_L))
+  
+  
+  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L)/(lm_sd_L)
+  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K)/(lm_sd_K)
+  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r)/(lm_sd_r)
+  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC)/(lm_sd_AUC)
+  
+  
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L,decreasing=TRUE),]
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG,decreasing=TRUE),]
+  write.csv(InteractionScores_RF,paste(outputpath,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  
+  for(i in 1:num_genes_RF){
+    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel,]
+    
+    p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg Zscore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm Zscore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_rf_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final,X_ZCalculations)
+    }
+  }
+  print("Pass RF ggplot loop")
+  write.csv(X_stats_interaction_ALL_RF_final,paste(outputpath,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  ####### Part 5 - Get Zscores for Gene deletion strains
+  
+  
+  
+  #get total number of genes for the next loop
+  num_genes <- length(unique(X2$OrfRep))
+  print(num_genes)
+  
+  #create the output data.frame containing columns for each deletion strain
+  InteractionScores <- unique(X2["OrfRep"])
+  #InteractionScores$Gene <- unique(X2$Gene)
+  InteractionScores$Gene <- NA
+  InteractionScores$Raw_Shift_L <- NA
+  InteractionScores$Z_Shift_L <- NA
+  InteractionScores$lm_Score_L <- NA
+  InteractionScores$Z_lm_L <- NA
+  InteractionScores$R_Squared_L <- NA
+  InteractionScores$Sum_Z_Score_L <- NA
+  InteractionScores$Avg_Zscore_L <- NA
+  InteractionScores$Raw_Shift_K <- NA
+  InteractionScores$Z_Shift_K <- NA
+  InteractionScores$lm_Score_K <- NA
+  InteractionScores$Z_lm_K <- NA
+  InteractionScores$R_Squared_K <- NA
+  InteractionScores$Sum_Z_Score_K <- NA
+  InteractionScores$Avg_Zscore_K <- NA
+  InteractionScores$Raw_Shift_r <- NA
+  InteractionScores$Z_Shift_r <- NA
+  InteractionScores$lm_Score_r <- NA
+  InteractionScores$Z_lm_r <- NA
+  InteractionScores$R_Squared_r <- NA
+  InteractionScores$Sum_Z_Score_r <- NA
+  InteractionScores$Avg_Zscore_r <- NA
+  InteractionScores$Raw_Shift_AUC <- NA
+  InteractionScores$Z_Shift_AUC <- NA
+  InteractionScores$lm_Score_AUC <- NA
+  InteractionScores$Z_lm_AUC <- NA
+  InteractionScores$R_Squared_AUC <- NA
+  InteractionScores$Sum_Z_Score_AUC <- NA
+  InteractionScores$Avg_Zscore_AUC <- NA
+  InteractionScores$NG <- NA
+  InteractionScores$DB <- NA
+  InteractionScores$SM <- NA
+  
+  for(i in 1:num_genes){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_L-lm_mean_L)/lm_sd_L
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_K-lm_mean_K)/lm_sd_K
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_r-lm_mean_r)/lm_sd_r
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_AUC-lm_mean_AUC)/lm_sd_AUC
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-NA
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction)
+    }
+    
+    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass Int Calculation loop")
+  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L,decreasing=TRUE),]
+  InteractionScores <- InteractionScores[order(InteractionScores$NG,decreasing=TRUE),]
+  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
+  #X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG,decreasing=TRUE),]
+  write.csv(InteractionScores,paste(outputpath,"ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_K <- InteractionScores[InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores_deletion_enhancers_L[!is.na(InteractionScores_deletion_enhancers_L$OrfRep),]
+  InteractionScores_deletion_enhancers_K <- InteractionScores_deletion_enhancers_K[!is.na(InteractionScores_deletion_enhancers_K$OrfRep),]
+  InteractionScores_deletion_suppressors_L <- InteractionScores_deletion_suppressors_L[!is.na(InteractionScores_deletion_suppressors_L$OrfRep),]
+  InteractionScores_deletion_suppressors_K <- InteractionScores_deletion_suppressors_K[!is.na(InteractionScores_deletion_suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores_deletion_enhancers_and_Suppressors_L[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores_deletion_enhancers_and_Suppressors_K[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE)
+  
+  #get enhancers and suppressors for linear regression
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2,]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores[InteractionScores$Z_lm_K <= -2,]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores_deletion_enhancers_L_lm[!is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores_deletion_enhancers_K_lm[!is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores_deletion_suppressors_L_lm[!is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores_deletion_suppressors_K_lm[!is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores_deletion_enhancers_and_Suppressors_L_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores_deletion_enhancers_and_Suppressors_K_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE)
+  
+  
+  write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+  print('ln 1570 write StudyInfo.csv after ')
+        #write.table(Labels,file=paste("../Code/StudyInfo.txt"),sep="\t",row.names = FALSE)
+  
+  for(i in 1:num_genes){
+    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel,]
+    
+    p_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_K,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final,X_ZCalculations)
+    }
+  }
+  print("Pass Int ggplot loop")
+  write.csv(X_stats_interaction_ALL_final,paste(outputpath,"ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  
+  
+  
+  
+  Blank <- ggplot(X2_RF) + geom_blank()
+  
+  pdf(paste(outputpath,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_l[[num]],p_K[[num]],p_r[[num]],p_AUC[[num]],p_l[[num+1]],p_K[[num+1]],p_r[[num+1]],p_AUC[[num+1]],p_l[[num+2]],p_K[[num+2]],p_r[[num+2]],p_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_l[[364]],p_K[[364]],p_r[[364]],p_l[[365]],p_K[[365]],p_r[[365]],p_l[[366]],p_K[[366]],p_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_K[[num+3]],p_r[[num+3]],p1[[num+4]],p_K[[num+4]],p_r[[num+4]]
+  }
+  if(num_genes != (num+2)){
+    total_num = num_genes - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],p_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  
+  
+  pdf(paste(outputpath,"RF_InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(12000,0)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes_RF)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes_RF)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_rf_l[[num]],p_rf_K[[num]],p_rf_r[[num]],p_rf_AUC[[num]],p_rf_l[[num+1]],p_rf_K[[num+1]],p_rf_r[[num+1]],p_rf_AUC[[num+1]],p_rf_l[[num+2]],p_rf_K[[num+2]],p_rf_r[[num+2]],p_rf_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_rf_l[[364]],p_rf_K[[364]],p_rf_r[[364]],p_rf_l[[365]],p_rf_K[[365]],p_rf_r[[365]],p_rf_l[[366]],p_rf_K[[366]],p_rf_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p1[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]]
+  }
+  if(num_genes_RF != (num+2)){
+    total_num = num_genes_RF - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],p_rf_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  #print rank plots for L and K gene interactions
+  
+  
+  InteractionScores_AdjustMissing <- InteractionScores
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L),]$Avg_Zscore_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K),]$Avg_Zscore_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r),]$Avg_Zscore_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC),]$Avg_Zscore_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank <- NA
+  InteractionScores_AdjustMissing$K_Rank <- NA
+  InteractionScores_AdjustMissing$r_Rank <- NA
+  InteractionScores_AdjustMissing$AUC_Rank <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
+  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
+  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
+  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
+  
+  #
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L),]$Z_lm_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K),]$Z_lm_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r),]$Z_lm_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC),]$Z_lm_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- NA
+  InteractionScores_AdjustMissing$K_Rank_lm <- NA
+  InteractionScores_AdjustMissing$r_Rank_lm <- NA
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
+  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
+  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
+  
+  
+  
+  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  
+  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,]
+  
+  #find overlaps
+  X_NArm$Overlap <- "No Effect"
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Both")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Both")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2,]$Overlap <- "Deletion Enhancer lm only")
+  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2,]$Overlap <- "Deletion Suppressor lm only")
+  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L <- lm(X_NArm$Z_lm_L~X_NArm$Avg_Zscore_L)
+  L_lm <- summary(get_lm_L)
+  
+  get_lm_K <- lm(X_NArm$Z_lm_K~X_NArm$Avg_Zscore_K)
+  K_lm <- summary(get_lm_K)
+  
+  get_lm_r <- lm(X_NArm$Z_lm_r~X_NArm$Avg_Zscore_r)
+  r_lm <- summary(get_lm_r)
+  
+  get_lm_AUC <- lm(X_NArm$Z_lm_AUC~X_NArm$Avg_Zscore_AUC)
+  AUC_lm <- summary(get_lm_AUC)
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm L") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_K,Z_lm_K)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm K") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(K_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_r,Z_lm_r)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm r") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(r_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_AUC,Z_lm_AUC)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm AUC") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(AUC_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  dev.off()
+  
+  
+  lm_v_Zscore_L <- ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L,ORF=OrfRep,Gene=Gene,NG=NG,SM=SM,DB=DB)) + geom_point(aes(color=Overlap),shape=3) + 
+    geom_smooth(method = "lm",color=1) +  ggtitle("Avg Zscore vs lm L") +
+    geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+    annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right()
+  
+  pgg <- ggplotly(lm_v_Zscore_L)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath,"Avg_Zscore_vs_lm_NA_rm.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
+  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
+  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
+  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
+  
+  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
+  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
+  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
+  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L2 <- lm(X_NArm$L_Rank_lm~X_NArm$L_Rank)
+  L_lm2 <- summary(get_lm_L2)
+  
+  get_lm_K2 <- lm(X_NArm$K_Rank_lm~X_NArm$K_Rank)
+  K_lm2 <- summary(get_lm_K2)
+  
+  get_lm_r2 <- lm(X_NArm$r_Rank_lm~X_NArm$r_Rank)
+  r_lm2 <- summary(get_lm_r2)
+  
+  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm~X_NArm$AUC_Rank)
+  AUC_lm2 <- summary(get_lm_AUC2)
+  
+  num_genes_NArm2 <- (dim(X_NArm)[1])/2
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_ranked_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm L") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(K_Rank,K_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm K") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(K_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(r_Rank,r_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm r") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(r_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(AUC_Rank,AUC_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank of Avg Zscore vs lm AUC") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(AUC_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  
+  
+  dev.off()
+  
+  
+  
+  Rank_L_1SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+}
+
+
+
+#get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+get_lm_1 <- lm(X_NArm$Z_lm_K~X_NArm$Z_lm_L)
+L_lm_1 <- summary(get_lm_1)
+
+get_lm_2 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_L)
+L_lm_2 <- summary(get_lm_2)
+
+get_lm_3 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_L)
+L_lm_3 <- summary(get_lm_3)
+
+get_lm_4 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_K)
+L_lm_4 <- summary(get_lm_4)
+
+get_lm_5 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_K)
+L_lm_5 <- summary(get_lm_5)
+
+get_lm_6 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_r)
+L_lm_6 <- summary(get_lm_6)
+
+
+pdf(file=paste(outputpath,"Correlation_CPPs.pdf",sep=""),width = 10, height = 7, onefile = TRUE)
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L),]
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_K),color="cyan") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_r),color="cyan") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_r),color="cyan") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_r,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+
+
+
+
+dev.off()
+
+
+#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE)
+timestamp()
+
+#BoneYard***********************************************
+#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced.
+#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. 
+#Corruptable but better than nothing.
+#if(is.na(Args[2])){
+# std=3
+#}else {
+# std= Arg[2]
+#Delta_Background_sdFactor <- 2  #Args[3]
+#DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#} 
+
diff --git a/workflow/.old/templates/qhtcp/Exp3/backups/InteractTemplateB4Prompt4SDinput.R b/workflow/.old/templates/qhtcp/Exp3/backups/InteractTemplateB4Prompt4SDinput.R
new file mode 100644
index 00000000..3b11777c
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp3/backups/InteractTemplateB4Prompt4SDinput.R
@@ -0,0 +1,2702 @@
+#Based on InteractionTemplate.R which is based on Sean Santose's Interaction_V5 script.
+#Adapt SS For Structured Data storage but using command line scripts
+###Set up the required libraries, call required plot theme elements and set up the command line arguments
+library("ggplot2")
+library("plyr")
+library("extrafont")
+library("gridExtra")
+library("gplots")
+library("RColorBrewer")
+library("stringr")
+#library("gdata")
+library(plotly)
+library(htmlwidgets)
+
+Args <- commandArgs(TRUE)
+input_file <- Args[1] #"!!Results_17_0827_yor1null-rpl12anull misLabeledAsFrom MI 17_0919_yor1-curated.txt"  #Args[1]  #Arg 1 #"!!ResultsStd_JS 19_1224_HLEG_P53.txt" is the !!results ... .txt
+
+#Path to Output Directory
+W=getwd() #R is F'd up, Can't use, Any legitamate platfold could build out dirs from this
+outDir <- "ZScores/" #"Args[2] #paste0(W,"/ZScores/")
+subDir <- outDir     #Args[2]
+
+if (file.exists(subDir)){
+  outputpath <- subDir
+} else {
+  dir.create(file.path(subDir))
+}
+
+if (file.exists(paste(subDir,"QC/",sep=""))){
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+} else {
+  dir.create(file.path(paste(subDir,"QC/",sep="")))
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+}
+#define the output path (formerly the second argument from Rscript)
+outputpath <- outDir 
+
+#Set Args[2] the Background contamination noise filter as a function of standard deviation
+#Sean recommends 3 or 5 SD factor.
+#Capture Exp_ number,use it to Save Args[2]{std}to Labels field and then Write to Labels to studyInfo.txt for future reference
+Labels <- read.csv(file= "../Code/StudyInfo.csv",stringsAsFactors = FALSE)  #,sep= ",")
+print("Be sure to include Argument 2 the Bacground noise filter standard deviation i.e., 3 or 5 per Sean")
+std= as.numeric(Args[2])
+expNumber<- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd()))
+Labels[expNumber,3]= as.numeric(std)
+Delta_Background_sdFactor <- std
+DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#Write Background SD value to studyInfo.txt file
+#write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+print('ln 50 write StudyInfo.csv ')
+#write.table(Labels,file=paste(outputpath,"StudyInfo.txt"),sep = "\t",row.names = FALSE)
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++BEGIN USER DATA SELECTION SECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+#read in the data
+X <- read.delim(input_file,skip=2,as.is=T,row.names=1,strip.white=TRUE)
+X <- X[!(X[[1]]%in%c("","Scan")),]
+#X <- X[!(X[[1]]%in%c(61:76)),]  #Remove dAmp plates which are Scans 61 thru 76
+
+#X <- X[which(X$Specifics == "WT"),]
+
+X_length <- length(X[1,])
+X_end <- length(X[1,]) - 2
+X <- X[,c(1:46,X_end:X_length)]
+
+
+#use numeric data to perform operations
+X$Col <- as.numeric(X$Col)
+X$Row <- as.numeric(X$Row)
+X$l <- as.numeric(X$l)
+X$K <- as.numeric(X$K)
+X$r <- as.numeric(X$r)
+X$Scan <- as.numeric(X$Scan)
+X$AUC <- as.numeric(X$AUC)
+X$LstBackgrd <- as.numeric(X$LstBackgrd)
+X$X1stBackgrd <- as.numeric(X$X1stBackgrd)
+
+#set the OrfRep to YDL227C for the ref data
+X[X$ORF == "YDL227C",]$OrfRep <- "YDL227C"
+#Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. 
+#That is the first DM plates are removed from the data set with the following.
+X <- X[X$Conc1 != "0ug/mL",] #This occurs only for Exp1 and Exp2 and so doesn't have any effect on Exp3&4
+
+
+#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. 
+#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used.
+#try(X <- X[X$ORF != "bad_spot",])
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=
+
+#get total number of drug concentrations
+Total_Conc_Nums <- length(unique(X$Conc))
+
+#function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
+numextract <- function(string){ 
+  str_extract(string, "\\-*\\d+\\.*\\d*")
+} 
+
+#generate a new column with the numeric drug concs
+X$Conc_Num <- as.numeric(numextract(X$Conc))
+#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
+X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1
+
+#Get the max factor for concentration
+MAX_CONC <- max(X$Conc_Num_Factor)
+#if treating numbers not as factors uncomment next line and comment out previous line
+#MAX_CONC <- max(X$Conc_Num)
+
+#remove wells with problems for making graphs and to not include in summary statistics
+X <- X[X$Gene != "BLANK",]
+X <- X[X$Gene != "Blank",] 
+X <- X[X$ORF != "Blank",]
+X <- X[X$Gene != "blank",]
+#X <- X[X$Gene != "HO",]
+Xbu= X
+#Inserted to use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script). This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
+#Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to write over the RF1&RF2 geneNames which caused a variance with his code results 
+#in the Z_lm_L,K,r&AUC output values. Values correlated well but were off by a multiplier factor.
+SGDgeneList= "../Code/SGD_features.tab"
+genes = data.frame(read.delim(file=SGDgeneList,quote="",header=FALSE,colClasses = c(rep("NULL",3), rep("character", 2), rep("NULL", 11))))
+for(i in 1:length(X[,14])){
+  ii= as.numeric(i)
+  line_num = match(X[ii,14],genes[,1],nomatch=1)
+  OrfRepColNum= as.numeric(match('OrfRep',names(X)))
+  if(X[ii,OrfRepColNum]!= "YDL227C"){
+     X[ii,15] = genes[line_num,2]
+  }
+  if((X[ii,15] == "")||(X[ii,15] == "OCT1")){
+    X[ii,15] = X[ii,OrfRepColNum]
+  }
+}
+Xblankreplace= X
+#X= Xbu #for restore testing restore X if geneName 'Match' routine needs changing
+
+#Remove dAmPs *******************************
+DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
+Damps <- read.delim(DAmPs_List,header=F)
+
+X <- X[!(X$ORF %in% Damps$V1),]  #fix this to Damps[,1]
+XafterDampsRM=X  #Backup for debugging especially when Rstudio goes crazy out of control
+# ***********
+
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++END USER DATA SELECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+print("ln137 End of User Section including blank gene writeOver")
+#++++Begin Graphics Boiler Plate Section+++++++++++++++++++++++++++++++++++++++
+#theme elements for plots
+theme_Publication <- function(base_size=14, base_family="sans") {
+  library(grid)
+  library(ggthemes)
+  (theme_foundation(base_size=base_size, base_family=base_family)
+    + theme(plot.title = element_text(face = "bold",
+                                      size = rel(1.2), hjust = 0.5),
+            text = element_text(),
+            panel.background = element_rect(colour = NA),
+            plot.background = element_rect(colour = NA),
+            panel.border = element_rect(colour = NA),
+            axis.title = element_text(face = "bold",size = rel(1)),
+            axis.title.y = element_text(angle=90,vjust =2),
+            axis.title.x = element_text(vjust = -0.2),
+            axis.text = element_text(), 
+            axis.line = element_line(colour="black"),
+            axis.ticks = element_line(),
+            panel.grid.major = element_line(colour="#f0f0f0"),
+            panel.grid.minor = element_blank(),
+            legend.key = element_rect(colour = NA),
+            legend.position = "bottom",
+            legend.direction = "horizontal",
+            legend.key.size= unit(0.2, "cm"),
+            legend.spacing = unit(0, "cm"),
+            legend.title = element_text(face="italic"),
+            plot.margin=unit(c(10,5,5,5),"mm"),
+            strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+            strip.text = element_text(face="bold")
+    ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  library(scales)
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
+  (theme_foundation(base_size=base_size, base_family=base_family)
+   + theme(plot.title = element_text(face = "bold",
+                                     size = rel(1.2), hjust = 0.5),
+           text = element_text(),
+           panel.background = element_rect(colour = NA),
+           plot.background = element_rect(colour = NA),
+           panel.border = element_rect(colour = NA),
+           axis.title = element_text(face = "bold",size = rel(1)),
+           axis.title.y = element_text(angle=90,vjust =2),
+           axis.title.x = element_text(vjust = -0.2),
+           axis.text = element_text(), 
+           axis.line = element_line(colour="black"),
+           axis.ticks = element_line(),
+           panel.grid.major = element_line(colour="#f0f0f0"),
+           panel.grid.minor = element_blank(),
+           legend.key = element_rect(colour = NA),
+           legend.position = "right",
+           legend.direction = "vertical",
+           legend.key.size= unit(0.5, "cm"),
+           legend.spacing = unit(0, "cm"),
+           legend.title = element_text(face="italic"),
+           plot.margin=unit(c(10,5,5,5),"mm"),
+           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+           strip.text = element_text(face="bold")
+   ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+#print timestamp for initial time the code starts
+timestamp()
+#+++++BEGIN QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+###Part 2 - Quality control
+#print quality control graphs for each dataset before removing data due to contamination
+#and before adjusting missing data to max theoretical values
+
+#plate analysis plot
+#plate analysis is a quality check to identify plate effects containing anomalies
+
+Plate_Analysis_L <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+
+
+Plate_Analysis_L_Box <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K_Box <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r_Box <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+#quality control - values with a high delta background likely have heavy contamination
+#check the frequency of these values
+#report the L and K values of these spots
+#report the number to be removed based on the Delta_Background_Tolerance
+X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd
+
+
+#raw l vs K before QC
+Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle("Raw L vs K before QC") +
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8)
+Raw_l_vs_K_beforeQC
+dev.off()
+pgg <- ggplotly(Raw_l_vs_K_beforeQC)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_beforeQC.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+
+#set delta background tolerance based on 3 sds from the mean delta background
+Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(DelBGFactr*sd(X$Delta_Backgrd))
+#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
+print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" "))
+
+Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+
+
+X_Delta_Backgrd_above_Tolerance <- X[X$Delta_Backgrd >= Delta_Background_Tolerance,]
+
+X_Delta_Backgrd_above_Tolerance_K_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$K,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_L_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$l,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_toRemove <- dim(X_Delta_Backgrd_above_Tolerance)[1]
+
+X_Delta_Backgrd_above_Tolerance_L_vs_K <- ggplot(X_Delta_Backgrd_above_Tolerance,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle(paste("Raw L vs K for strains above delta background threshold of",Delta_Background_Tolerance,"or above")) +
+  annotate("text",x=X_Delta_Backgrd_above_Tolerance_L_halfmedian,y=X_Delta_Backgrd_above_Tolerance_K_halfmedian,
+           label = paste("Strains above delta background tolerance = ",X_Delta_Backgrd_above_Tolerance_toRemove)) + 
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8)
+X_Delta_Backgrd_above_Tolerance_L_vs_K
+dev.off()
+pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+#frequency plot for all data vs. the delta_background
+DeltaBackground_Frequency_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_density() +
+  ggtitle("Density plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+#bar plot for all data vs. the delta_background
+DeltaBackground_Bar_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_bar() +
+  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+pdf(file = paste(outputpath_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8)
+print(DeltaBackground_Frequency_Plot)
+print(DeltaBackground_Bar_Plot)
+dev.off()
+
+
+#Need to identify missing data, and differentiate between this data and removed data so the removed data can get set to NA and the missing data can get set to max theoretical values
+#1 for missing data, 0 for non missing data
+#Use "NG" for NoGrowth rather than "missing"
+X$NG <- 0
+try(X[X$l == 0 & !is.na(X$l),]$NG <- 1)
+
+#1 for removed data, 0 non removed data
+#Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
+X$DB <- 0
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$DB <- 1)
+
+#replace the CPPs for l, r, AUC and K (must be last!) for removed data
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$l <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$r <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$AUC <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$K <- NA)
+
+
+Plate_Analysis_L_afterQC <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L
+Plate_Analysis_L_afterQC
+Plate_Analysis_K
+Plate_Analysis_K_afterQC
+Plate_Analysis_r
+Plate_Analysis_r_afterQC
+Plate_Analysis_AUC
+Plate_Analysis_AUC_afterQC
+Plate_Analysis_Delta_Backgrd
+Plate_Analysis_Delta_Backgrd_afterQC
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box
+Plate_Analysis_L_Box_afterQC
+Plate_Analysis_K_Box
+Plate_Analysis_K_Box_afterQC
+Plate_Analysis_r_Box
+Plate_Analysis_r_Box_afterQC
+Plate_Analysis_AUC_Box
+Plate_Analysis_AUC_Box_afterQC
+Plate_Analysis_Delta_Backgrd_Box
+Plate_Analysis_Delta_Backgrd_Box_afterQC
+dev.off()
+
+#remove the zero values and print plate analysis
+X_noZero <- X[which(X$l > 0),]
+Plate_Analysis_L_afterQC_Z <- ggplot(X_noZero,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC_Z <- ggplot(X_noZero,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC_Z <- ggplot(X_noZero,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC_Z <- ggplot(X_noZero,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC_Z <- ggplot(X_noZero,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L_afterQC_Z
+Plate_Analysis_K_afterQC_Z
+Plate_Analysis_r_afterQC_Z
+Plate_Analysis_AUC_afterQC_Z
+Plate_Analysis_Delta_Backgrd_afterQC_Z
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box_afterQC_Z
+Plate_Analysis_K_Box_afterQC_Z
+Plate_Analysis_r_Box_afterQC_Z
+Plate_Analysis_AUC_Box_afterQC_Z
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
+dev.off()
+
+#remove dataset with zeros removed
+rm(X_noZero)
+
+
+#X_test_missing_and_removed <- X[X$Removed == 1,]
+
+#calculate summary statistics for all strains, including both background and the deletions
+X_stats_ALL <- ddply(X, c("Conc_Num","Conc_Num_Factor"), summarise,
+                     N = (length(l)),
+                     mean_L = mean(l,na.rm=TRUE),
+                     median_L = median(l,na.rm=TRUE),
+                     max_L = max(l,na.rm=TRUE),
+                     min_L = min(l,na.rm=TRUE),
+                     sd_L = sd(l,na.rm=TRUE),
+                     se_L = sd_L / sqrt(N-1),
+                     mean_K = mean(K,na.rm=TRUE),
+                     median_K = median(K,na.rm=TRUE),
+                     max_K = max(K,na.rm=TRUE),
+                     min_K = min(K,na.rm=TRUE),
+                     sd_K = sd(K,na.rm=TRUE),
+                     se_K = sd_K / sqrt(N-1),
+                     mean_r = mean(r,na.rm=TRUE),
+                     median_r = median(r,na.rm=TRUE),
+                     max_r = max(r,na.rm=TRUE),
+                     min_r = min(r,na.rm=TRUE),
+                     sd_r = sd(r,na.rm=TRUE),
+                     se_r = sd_r / sqrt(N-1),
+                     mean_AUC = mean(AUC,na.rm=TRUE),
+                     median_AUC = median(AUC,na.rm=TRUE),
+                     max_AUC = max(AUC,na.rm=TRUE),
+                     min_AUC = min(AUC,na.rm=TRUE),
+                     sd_AUC = sd(AUC,na.rm=TRUE),
+                     se_AUC = sd_AUC / sqrt(N-1)
+)
+#print(X_stats_ALL_L)
+
+write.csv(X_stats_ALL,file=paste(outputpath,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE)
+#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+##### Part 3 - Generate summary statistics and calculate the max theoretical L value
+##### Calculate the Z score at each drug conc for each deletion strain
+
+
+#get the background strains - can be modified to take another argument but for most screens will just be YDL227C
+Background_Strains <- c("YDL227C")
+
+#first part of loop will go through for each background strain 
+#most cases there will only be one YDL227C
+for(s in Background_Strains){
+  X_Background <- X[X$OrfRep == s,]
+  
+  #if there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
+  #we may want to consider in some cases giving the max high value to L depending on the data type
+  if(table(X_Background$l)[1] == 0){
+    X_Background[X_Background$l == 0,]$l <- NA
+    X_Background[X_Background$K == 0,]$K <- NA
+    X_Background[X_Background$r == 0,]$r <- NA
+    X_Background[X_Background$AUC == 0,]$AUC <- NA
+  }
+  
+  X_Background <- X_Background[!is.na(X_Background$l),]
+  
+  #get summary stats for L, K, R, AUC
+  X_stats_BY_L <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(l)),
+                        mean = mean(l,na.rm=TRUE),
+                        median = median(l,na.rm=TRUE),
+                        max = max(l,na.rm=TRUE),
+                        min = min(l,na.rm=TRUE),
+                        sd = sd(l,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L)
+  X1_SD <- max(X_stats_BY_L$sd)
+  
+  X_stats_BY_K <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(K)),
+                        mean = mean(K,na.rm=TRUE),
+                        median = median(K,na.rm=TRUE),
+                        max = max(K,na.rm=TRUE),
+                        min = min(K,na.rm=TRUE),
+                        sd = sd(K,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_K <- max(X_stats_BY_K$sd)
+  
+  
+  X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = length(r),
+                        mean = mean(r,na.rm=TRUE),
+                        median = median(r,na.rm=TRUE),
+                        max = max(r,na.rm=TRUE),
+                        min = min(r,na.rm=TRUE),
+                        sd = sd(r,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_r <- max(X_stats_BY_r$sd)
+  
+  X_stats_BY_AUC <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                          N = length(AUC),
+                          mean = mean(AUC,na.rm=TRUE),
+                          median = median(AUC,na.rm=TRUE),
+                          max = max(AUC,na.rm=TRUE),
+                          min = min(AUC,na.rm=TRUE),
+                          sd = sd(AUC,na.rm=TRUE),
+                          se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
+  
+  X_stats_BY <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                      N = (length(l)),
+                      mean_L = mean(l,na.rm=TRUE),
+                      median_L = median(l,na.rm=TRUE),
+                      max_L = max(l,na.rm=TRUE),
+                      min_L = min(l,na.rm=TRUE),
+                      sd_L = sd(l,na.rm=TRUE),
+                      se_L = sd_L / sqrt(N-1),
+                      mean_K = mean(K,na.rm=TRUE),
+                      median_K = median(K,na.rm=TRUE),
+                      max_K = max(K,na.rm=TRUE),
+                      min_K = min(K,na.rm=TRUE),
+                      sd_K = sd(K,na.rm=TRUE),
+                      se_K = sd_K / sqrt(N-1),
+                      mean_r = mean(r,na.rm=TRUE),
+                      median_r = median(r,na.rm=TRUE),
+                      max_r = max(r,na.rm=TRUE),
+                      min_r = min(r,na.rm=TRUE),
+                      sd_r = sd(r,na.rm=TRUE),
+                      se_r = sd_r / sqrt(N-1),
+                      mean_AUC = mean(AUC,na.rm=TRUE),
+                      median_AUC = median(AUC,na.rm=TRUE),
+                      max_AUC = max(AUC,na.rm=TRUE),
+                      min_L = min(AUC,na.rm=TRUE),
+                      sd_AUC = sd(AUC,na.rm=TRUE),
+                      se_AUC = sd_AUC / sqrt(N-1)
+  )
+  
+  write.csv(X_stats_BY,file=paste(outputpath,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE)
+  
+  #calculate the max theoretical L values
+  #only look for max values when K is within 2SD of the ref strain
+  for(q in unique(X$Conc_Num_Factor)){
+    if(q == 0){
+      X_within_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[!is.na(X_within_2SD_K$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) ,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+      X_outside_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[!is.na(X_outside_2SD_K$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1]))  ,]
+      #X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+    }
+    if(q > 0){
+      X_within_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K <- rbind(X_within_2SD_K,X_within_2SD_K_temp)
+      
+      X_outside_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      #X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      X_outside_2SD_K <- rbind(X_outside_2SD_K,X_outside_2SD_K_temp)
+    }
+  }
+  
+  X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                     N = (length(l)),
+                                     mean = mean(l),
+                                     median = median(l),
+                                     max = max(l,na.rm=TRUE),
+                                     min = min(l,na.rm=TRUE),
+                                     sd = sd(l),
+                                     se = sd / sqrt(N-1),
+                                     z_max = (max-mean)/sd
+  )
+  print(X_stats_BY_L_within_2SD_K)
+  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)
+  write.csv(X_stats_BY_L_within_2SD_K,file=paste(outputpath_QC,"Max_Observed_L_Vals_for_spots_within_2SD_K.csv",sep=""),row.names=FALSE)
+  
+  X_stats_BY_L_outside_2SD_K <- ddply(X_outside_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                      N = (length(l)),
+                                      mean = mean(l),
+                                      median = median(l),
+                                      max = max(l,na.rm=TRUE),
+                                      min = min(l,na.rm=TRUE),
+                                      sd = sd(l),
+                                      se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L_outside_2SD_K)
+  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)
+  
+  #X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l,]
+  Outside_2SD_K_L_vs_K <- ggplot(X_outside_2SD_K,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  print(Outside_2SD_K_L_vs_K)
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"RawL_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  Outside_2SD_K_delta_background_vs_K <- ggplot(X_outside_2SD_K,aes(Delta_Backgrd,K,color=as.factor(Conc_Num))) + geom_point(aes(l=l,ORF=ORF,Gene=Gene),shape=3,position="jitter") +
+    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  Outside_2SD_K_delta_background_vs_K
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"DeltaBackground_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  #get the background strain mean values at the no drug conc to calculate shift
+  Background_L <- X_stats_BY_L$mean[1]
+  Background_K <- X_stats_BY_K$mean[1]
+  Background_r <- X_stats_BY_r$mean[1]
+  Background_AUC <- X_stats_BY_AUC$mean[1]
+  
+  #create empty plots for plotting element
+  p_l <-  ggplot()
+  p_K <-  ggplot()
+  p_r <-  ggplot()
+  p_AUC <-  ggplot()
+  
+  p_rf_l <-  ggplot()
+  p_rf_K <-  ggplot()
+  p_rf_r <-  ggplot()
+  p_rf_AUC <-  ggplot()
+  
+  #get only the deletion strains
+  X2 <- X
+  X2 <- X2[X2$OrfRep != "YDL227C",]
+  
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2$Conc_Num))){
+    Concentration <- unique(X2$Conc_Num)[i]
+    X2_temp <- X2[X2$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_new <- X2_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      
+      #X2_temp[X2_temp$K == 0,]$K <- X_stats_ALL_K$max[i]
+      #X2_temp[X2_temp$r == 0,]$r <- X_stats_ALL_r$max[i]
+      #X2_temp[X2_temp$AUC == 0,]$AUC <- X_stats_ALL_AUC$max[i]
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+      
+      X2_new <- rbind(X2_new,X2_temp)
+      
+    }
+  }
+  X2 <- X2_new
+  
+  
+  #get only the RF strains
+  X2_RF <- X
+  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",]
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2_RF$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2_RF$Conc_Num))){
+    Concentration <- unique(X2_RF$Conc_Num)[i]
+    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_RF_new <- X2_RF_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      print(paste("Check loop order, if error, refs have no L values outside theoretical max L, for REFs, conc =",Concentration,sep=" "))
+      
+      X2_RF_new <- rbind(X2_RF_new,X2_RF_temp)
+      
+    }
+  }
+  X2_RF <- X2_RF_new
+  
+  
+  #######Part 4 Get the RF Z score values
+  #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
+  X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_")
+  
+  num_genes_RF <- length(unique(X2_RF$OrfRep))
+  print(num_genes_RF)
+  
+  
+  #create the output data.frame containing columns for each RF strain
+  InteractionScores_RF <- unique(X2_RF["OrfRep"])
+  #InteractionScores_RF$Gene <- unique(X2$Gene)
+  InteractionScores_RF$Gene <- NA
+  InteractionScores_RF$Raw_Shift_L <- NA
+  InteractionScores_RF$Z_Shift_L <- NA
+  InteractionScores_RF$lm_Score_L <- NA
+  InteractionScores_RF$Z_lm_L <- NA  
+  InteractionScores_RF$R_Squared_L <- NA
+  InteractionScores_RF$Sum_Z_Score_L <- NA
+  InteractionScores_RF$Avg_Zscore_L <- NA
+  InteractionScores_RF$Raw_Shift_K <- NA
+  InteractionScores_RF$Z_Shift_K <- NA
+  InteractionScores_RF$lm_Score_K <- NA
+  InteractionScores_RF$Z_lm_K <- NA  
+  InteractionScores_RF$R_Squared_K <- NA
+  InteractionScores_RF$Sum_Z_Score_K <- NA
+  InteractionScores_RF$Avg_Zscore_K <- NA
+  InteractionScores_RF$Raw_Shift_r <- NA
+  InteractionScores_RF$Z_Shift_r <- NA
+  InteractionScores_RF$lm_Score_r <- NA
+  InteractionScores_RF$Z_lm_r <- NA
+  InteractionScores_RF$R_Squared_r <- NA
+  InteractionScores_RF$Sum_Z_Score_r <- NA
+  InteractionScores_RF$Avg_Zscore_r <- NA
+  InteractionScores_RF$Raw_Shift_AUC <- NA
+  InteractionScores_RF$Z_Shift_AUC <- NA
+  InteractionScores_RF$lm_Score_AUC <- NA
+  InteractionScores_RF$Z_lm_AUC <- NA
+  InteractionScores_RF$R_Squared_AUC <- NA
+  InteractionScores_RF$Sum_Z_Score_AUC <- NA
+  InteractionScores_RF$Avg_Zscore_AUC <- NA
+  InteractionScores_RF$NG <- NA
+  InteractionScores_RF$SM <- NA
+  
+  
+  for(i in 1:num_genes_RF){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2_RF$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      gene_lm_AUC <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      gene_interaction_AUC <- NA
+      r_squared_AUC <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction)
+    }
+    
+    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass RF Calculation loop")
+  
+  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  print(paste("Mean RF linear regression score L",lm_mean_L))
+  
+  
+  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L)/(lm_sd_L)
+  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K)/(lm_sd_K)
+  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r)/(lm_sd_r)
+  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC)/(lm_sd_AUC)
+  
+  
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L,decreasing=TRUE),]
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG,decreasing=TRUE),]
+  write.csv(InteractionScores_RF,paste(outputpath,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  
+  for(i in 1:num_genes_RF){
+    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel,]
+    
+    p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg Zscore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm Zscore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_rf_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final,X_ZCalculations)
+    }
+  }
+  print("Pass RF ggplot loop")
+  write.csv(X_stats_interaction_ALL_RF_final,paste(outputpath,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  ####### Part 5 - Get Zscores for Gene deletion strains
+  
+  
+  
+  #get total number of genes for the next loop
+  num_genes <- length(unique(X2$OrfRep))
+  print(num_genes)
+  
+  #create the output data.frame containing columns for each deletion strain
+  InteractionScores <- unique(X2["OrfRep"])
+  #InteractionScores$Gene <- unique(X2$Gene)
+  InteractionScores$Gene <- NA
+  InteractionScores$Raw_Shift_L <- NA
+  InteractionScores$Z_Shift_L <- NA
+  InteractionScores$lm_Score_L <- NA
+  InteractionScores$Z_lm_L <- NA
+  InteractionScores$R_Squared_L <- NA
+  InteractionScores$Sum_Z_Score_L <- NA
+  InteractionScores$Avg_Zscore_L <- NA
+  InteractionScores$Raw_Shift_K <- NA
+  InteractionScores$Z_Shift_K <- NA
+  InteractionScores$lm_Score_K <- NA
+  InteractionScores$Z_lm_K <- NA
+  InteractionScores$R_Squared_K <- NA
+  InteractionScores$Sum_Z_Score_K <- NA
+  InteractionScores$Avg_Zscore_K <- NA
+  InteractionScores$Raw_Shift_r <- NA
+  InteractionScores$Z_Shift_r <- NA
+  InteractionScores$lm_Score_r <- NA
+  InteractionScores$Z_lm_r <- NA
+  InteractionScores$R_Squared_r <- NA
+  InteractionScores$Sum_Z_Score_r <- NA
+  InteractionScores$Avg_Zscore_r <- NA
+  InteractionScores$Raw_Shift_AUC <- NA
+  InteractionScores$Z_Shift_AUC <- NA
+  InteractionScores$lm_Score_AUC <- NA
+  InteractionScores$Z_lm_AUC <- NA
+  InteractionScores$R_Squared_AUC <- NA
+  InteractionScores$Sum_Z_Score_AUC <- NA
+  InteractionScores$Avg_Zscore_AUC <- NA
+  InteractionScores$NG <- NA
+  InteractionScores$DB <- NA
+  InteractionScores$SM <- NA
+  
+  for(i in 1:num_genes){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_L-lm_mean_L)/lm_sd_L
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_K-lm_mean_K)/lm_sd_K
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_r-lm_mean_r)/lm_sd_r
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_AUC-lm_mean_AUC)/lm_sd_AUC
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-NA
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction)
+    }
+    
+    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass Int Calculation loop")
+  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L,decreasing=TRUE),]
+  InteractionScores <- InteractionScores[order(InteractionScores$NG,decreasing=TRUE),]
+  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
+  #X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG,decreasing=TRUE),]
+  write.csv(InteractionScores,paste(outputpath,"ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_K <- InteractionScores[InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores_deletion_enhancers_L[!is.na(InteractionScores_deletion_enhancers_L$OrfRep),]
+  InteractionScores_deletion_enhancers_K <- InteractionScores_deletion_enhancers_K[!is.na(InteractionScores_deletion_enhancers_K$OrfRep),]
+  InteractionScores_deletion_suppressors_L <- InteractionScores_deletion_suppressors_L[!is.na(InteractionScores_deletion_suppressors_L$OrfRep),]
+  InteractionScores_deletion_suppressors_K <- InteractionScores_deletion_suppressors_K[!is.na(InteractionScores_deletion_suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores_deletion_enhancers_and_Suppressors_L[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores_deletion_enhancers_and_Suppressors_K[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE)
+  
+  #get enhancers and suppressors for linear regression
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2,]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores[InteractionScores$Z_lm_K <= -2,]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores_deletion_enhancers_L_lm[!is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores_deletion_enhancers_K_lm[!is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores_deletion_suppressors_L_lm[!is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores_deletion_suppressors_K_lm[!is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores_deletion_enhancers_and_Suppressors_L_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores_deletion_enhancers_and_Suppressors_K_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE)
+  
+  
+  write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+  print('ln 1570 write StudyInfo.csv after ')
+        #write.table(Labels,file=paste("../Code/StudyInfo.txt"),sep="\t",row.names = FALSE)
+  
+  for(i in 1:num_genes){
+    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel,]
+    
+    p_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_K,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final,X_ZCalculations)
+    }
+  }
+  print("Pass Int ggplot loop")
+  write.csv(X_stats_interaction_ALL_final,paste(outputpath,"ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  
+  
+  
+  
+  Blank <- ggplot(X2_RF) + geom_blank()
+  
+  pdf(paste(outputpath,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_l[[num]],p_K[[num]],p_r[[num]],p_AUC[[num]],p_l[[num+1]],p_K[[num+1]],p_r[[num+1]],p_AUC[[num+1]],p_l[[num+2]],p_K[[num+2]],p_r[[num+2]],p_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_l[[364]],p_K[[364]],p_r[[364]],p_l[[365]],p_K[[365]],p_r[[365]],p_l[[366]],p_K[[366]],p_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_K[[num+3]],p_r[[num+3]],p1[[num+4]],p_K[[num+4]],p_r[[num+4]]
+  }
+  if(num_genes != (num+2)){
+    total_num = num_genes - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],p_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  
+  
+  pdf(paste(outputpath,"RF_InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(12000,0)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes_RF)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes_RF)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_rf_l[[num]],p_rf_K[[num]],p_rf_r[[num]],p_rf_AUC[[num]],p_rf_l[[num+1]],p_rf_K[[num+1]],p_rf_r[[num+1]],p_rf_AUC[[num+1]],p_rf_l[[num+2]],p_rf_K[[num+2]],p_rf_r[[num+2]],p_rf_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_rf_l[[364]],p_rf_K[[364]],p_rf_r[[364]],p_rf_l[[365]],p_rf_K[[365]],p_rf_r[[365]],p_rf_l[[366]],p_rf_K[[366]],p_rf_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p1[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]]
+  }
+  if(num_genes_RF != (num+2)){
+    total_num = num_genes_RF - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],p_rf_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  #print rank plots for L and K gene interactions
+  
+  
+  InteractionScores_AdjustMissing <- InteractionScores
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L),]$Avg_Zscore_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K),]$Avg_Zscore_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r),]$Avg_Zscore_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC),]$Avg_Zscore_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank <- NA
+  InteractionScores_AdjustMissing$K_Rank <- NA
+  InteractionScores_AdjustMissing$r_Rank <- NA
+  InteractionScores_AdjustMissing$AUC_Rank <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
+  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
+  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
+  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
+  
+  #
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L),]$Z_lm_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K),]$Z_lm_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r),]$Z_lm_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC),]$Z_lm_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- NA
+  InteractionScores_AdjustMissing$K_Rank_lm <- NA
+  InteractionScores_AdjustMissing$r_Rank_lm <- NA
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
+  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
+  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
+  
+  
+  
+  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  
+  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,]
+  
+  #find overlaps
+  X_NArm$Overlap <- "No Effect"
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Both")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Both")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2,]$Overlap <- "Deletion Enhancer lm only")
+  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2,]$Overlap <- "Deletion Suppressor lm only")
+  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L <- lm(X_NArm$Z_lm_L~X_NArm$Avg_Zscore_L)
+  L_lm <- summary(get_lm_L)
+  
+  get_lm_K <- lm(X_NArm$Z_lm_K~X_NArm$Avg_Zscore_K)
+  K_lm <- summary(get_lm_K)
+  
+  get_lm_r <- lm(X_NArm$Z_lm_r~X_NArm$Avg_Zscore_r)
+  r_lm <- summary(get_lm_r)
+  
+  get_lm_AUC <- lm(X_NArm$Z_lm_AUC~X_NArm$Avg_Zscore_AUC)
+  AUC_lm <- summary(get_lm_AUC)
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm L") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_K,Z_lm_K)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm K") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(K_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_r,Z_lm_r)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm r") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(r_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_AUC,Z_lm_AUC)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm AUC") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(AUC_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  dev.off()
+  
+  
+  lm_v_Zscore_L <- ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L,ORF=OrfRep,Gene=Gene,NG=NG,SM=SM,DB=DB)) + geom_point(aes(color=Overlap),shape=3) + 
+    geom_smooth(method = "lm",color=1) +  ggtitle("Avg Zscore vs lm L") +
+    geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+    annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right()
+  
+  pgg <- ggplotly(lm_v_Zscore_L)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath,"Avg_Zscore_vs_lm_NA_rm.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
+  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
+  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
+  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
+  
+  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
+  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
+  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
+  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L2 <- lm(X_NArm$L_Rank_lm~X_NArm$L_Rank)
+  L_lm2 <- summary(get_lm_L2)
+  
+  get_lm_K2 <- lm(X_NArm$K_Rank_lm~X_NArm$K_Rank)
+  K_lm2 <- summary(get_lm_K2)
+  
+  get_lm_r2 <- lm(X_NArm$r_Rank_lm~X_NArm$r_Rank)
+  r_lm2 <- summary(get_lm_r2)
+  
+  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm~X_NArm$AUC_Rank)
+  AUC_lm2 <- summary(get_lm_AUC2)
+  
+  num_genes_NArm2 <- (dim(X_NArm)[1])/2
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_ranked_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm L") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(K_Rank,K_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm K") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(K_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(r_Rank,r_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm r") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(r_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(AUC_Rank,AUC_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank of Avg Zscore vs lm AUC") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(AUC_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  
+  
+  dev.off()
+  
+  
+  
+  Rank_L_1SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+}
+
+
+
+#get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+get_lm_1 <- lm(X_NArm$Z_lm_K~X_NArm$Z_lm_L)
+L_lm_1 <- summary(get_lm_1)
+
+get_lm_2 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_L)
+L_lm_2 <- summary(get_lm_2)
+
+get_lm_3 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_L)
+L_lm_3 <- summary(get_lm_3)
+
+get_lm_4 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_K)
+L_lm_4 <- summary(get_lm_4)
+
+get_lm_5 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_K)
+L_lm_5 <- summary(get_lm_5)
+
+get_lm_6 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_r)
+L_lm_6 <- summary(get_lm_6)
+
+
+pdf(file=paste(outputpath,"Correlation_CPPs.pdf",sep=""),width = 10, height = 7, onefile = TRUE)
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L),]
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_K),color="cyan") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_r),color="cyan") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_r),color="cyan") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_r,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+
+
+
+
+dev.off()
+
+
+#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE)
+timestamp()
+
+#BoneYard***********************************************
+#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced.
+#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. 
+#Corruptable but better than nothing.
+#if(is.na(Args[2])){
+# std=3
+#}else {
+# std= Arg[2]
+#Delta_Background_sdFactor <- 2  #Args[3]
+#DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#} 
+
diff --git a/workflow/.old/templates/qhtcp/Exp4/.DS_Store b/workflow/.old/templates/qhtcp/Exp4/.DS_Store
new file mode 100644
index 00000000..407f212c
Binary files /dev/null and b/workflow/.old/templates/qhtcp/Exp4/.DS_Store differ
diff --git a/workflow/.old/templates/qhtcp/Exp4/ExpFrontend.m b/workflow/.old/templates/qhtcp/Exp4/ExpFrontend.m
new file mode 100644
index 00000000..2bdc98c3
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp4/ExpFrontend.m
@@ -0,0 +1,53 @@
+%FrontEnd utility to copy source result sheet into Exp_ folders of
+%StudiesQHTCP/StudyName/Exp1(2,3,4). This allow the automation of path
+%capture to the StudiesDataArchieve.txt study log.
+%Select, copy and Capture Study Exp_ details to study log
+
+%Exp meta data collection 
+W=pwd;
+%Load matlab stored data file into workspace
+try 
+ExpLabel= strcat('Exp',W(end))
+questdlg('\fontsize{20} Select the !!Results File','File Selection','OK', struct('Default','OK','Interpreter','tex'));
+[resFile,resPath]= uigetfile('*.txt')
+copyfile((fullfile(resPath,resFile)),fullfile(W))
+resDate= char(regexp(resFile, '(\d\d\_\d\d\d\d)|( \d\d\_\d\d\d\d|\d\d\d\d\d\d)','match'))
+cd ..
+Wstudy= pwd
+studyDate= datetime("today");
+S.sDate(1) = {studyDate};
+if ispc
+    lastSep=max(strfind(Wstudy,'\'))
+    studyName= Wstudy((lastSep+1):end)
+else
+    lastSep=max(strfind(Wstudy,'/'))
+    studyName= Wstudy((lastSep+1):end)
+end
+
+S.sDate(1)= {studyDate};
+S.sName(1)= {studyName}
+S.sPath(1)= {Wstudy}
+S.ELabel(1)= {ExpLabel}
+S.EresDate(1)= {resDate}
+S.EresFile(1)= {resFile}
+S.EresPath(1)= {resPath}
+   
+cd ..
+
+fid = fopen('StudiesDataArchive.txt','a');  
+fprintf(fid,'StudyDate\tStudyName\tStudyPath\tExpNum\tExpDate\tExpPath\tResultFile\n');
+fprintf(fid, '%s\t %s\t %s\t %s\t %s\t %s\t %s \n',S.sDate{1},S.sName{1},S.sPath{1},S.ELabel{1},S.EresDate{1},S.EresPath{1},S.EresFile{1});
+fclose(fid);
+%save((fullfile(pwd,'studyLog.mat')), 'S')  
+fclose('all');
+
+catch
+    cd(W)
+    disp('Error: Unable to Execute ExpFrontend.m')
+end
+
+
+
+
+
+
diff --git a/workflow/.old/templates/qhtcp/Exp4/NotesExp4 b/workflow/.old/templates/qhtcp/Exp4/NotesExp4
new file mode 100644
index 00000000..e69de29b
diff --git a/workflow/.old/templates/qhtcp/Exp4/Z_InteractionTemplate.R b/workflow/.old/templates/qhtcp/Exp4/Z_InteractionTemplate.R
new file mode 100644
index 00000000..fe31ca14
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp4/Z_InteractionTemplate.R
@@ -0,0 +1,2730 @@
+#Based on InteractionTemplate.R which is based on Sean Santose's Interaction_V5 script.
+#Adapt SS For Structured Data storage but using command line scripts
+###Set up the required libraries, call required plot theme elements and set up the command line arguments
+library("ggplot2")
+library("plyr")
+library("extrafont")
+library("gridExtra")
+library("gplots")
+library("RColorBrewer")
+library("stringr")
+#library("gdata")
+library(plotly)
+library(htmlwidgets)
+
+Args <- commandArgs(TRUE)
+input_file <- Args[1] #"!!Results_17_0827_yor1null-rpl12anull misLabeledAsFrom MI 17_0919_yor1-curated.txt"  #Args[1]  #Arg 1 #"!!ResultsStd_JS 19_1224_HLEG_P53.txt" is the !!results ... .txt
+#Tool to find a file and copy it to desired location
+destDir= getwd()
+#srcFile= file.choose()
+#file.copy(srcFile, destDir)
+#input_file= tail(strsplit(srcFile,"[/]")[[1]],1)
+
+
+
+#Path to Output Directory
+#W=getwd() #R is F'd up, Can't use, Any legitamate platform could build out dirs from this
+outDir <- "ZScores/" #"Args[2] #paste0(W,"/ZScores/")
+subDir <- outDir     #Args[2]
+
+if (file.exists(subDir)){
+  outputpath <- subDir
+} else {
+  dir.create(file.path(subDir))
+}
+
+if (file.exists(paste(subDir,"QC/",sep=""))){
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+} else {
+  dir.create(file.path(paste(subDir,"QC/",sep="")))
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+}
+#define the output path (formerly the second argument from Rscript)
+outputpath <- outDir 
+
+#Set Args[2] the Background contamination noise filter as a function of standard deviation
+#std= as.numeric(Args[2])
+#Sean recommends 3 or 5 SD factor.
+#Capture Exp_ number,use it to Save Args[2]{std}to Labels field and then Write to Labels to studyInfo.txt for future reference
+Labels <- read.csv(file= "../Code/StudyInfo.csv",stringsAsFactors = FALSE)  #,sep= ",")
+print("Be sure to enter Background noise filter standard deviation i.e., 3 or 5 per Sean")
+
+#User prompt for std multiplier Value
+cat("Enter a Standard Deviation value to noise filter \n")
+inpChar<- readLines(file("stdin"), n = 1L)
+cat(paste("Standard Deviation Value is", inpChar, "\n"))
+inpNum= as.numeric(inpChar)
+#set std deviation multiplier default if no user entry
+if(!is.na(inpNum)){
+  std= inpNum
+}else{std= 3}
+
+
+expNumber<- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd()))
+Labels[expNumber,3]= as.numeric(std)
+Delta_Background_sdFactor <- std
+DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#Write Background SD value to studyInfo.txt file
+#write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+print('ln 50 write StudyInfo.csv ')
+#write.table(Labels,file=paste(outputpath,"StudyInfo.txt"),sep = "\t",row.names = FALSE)
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++BEGIN USER DATA SELECTION SECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+
+#read in the data
+X <- read.delim(input_file,skip=2,as.is=T,row.names=1,strip.white=TRUE)
+X <- X[!(X[[1]]%in%c("","Scan")),]
+#X <- X[!(X[[1]]%in%c(61:76)),]  #Remove dAmp plates which are Scans 61 thru 76
+
+#X <- X[which(X$Specifics == "WT"),]
+
+#X_length <- length(X[1,])
+#X_end <- length(X[1,]) - 2
+#X <- X[,c(1:42,X_end:X_length)]
+
+
+#use numeric data to perform operations
+X$Col <- as.numeric(X$Col)
+X$Row <- as.numeric(X$Row)
+X$l <- as.numeric(X$l)
+X$K <- as.numeric(X$K)
+X$r <- as.numeric(X$r)
+X$Scan <- as.numeric(X$Scan)
+X$AUC <- as.numeric(X$AUC)
+X$LstBackgrd <- as.numeric(X$LstBackgrd)
+X$X1stBackgrd <- as.numeric(X$X1stBackgrd)
+
+#Sometimes the an experimenter may have placed the non-varying drug in the 'Drug' col instead of the 'Modifier1' col
+#as was the case in Gemcitabin and Cytarabin experiments.
+#The following allows user to rename columns so as to get the appropriate
+#data where it needs to be for the script to run properly.
+#colnames(X)[7]  <- "Modifier1"
+#colnames(X)[8]  <- "Conc1"
+#colnames(X)[10]  <- "Drug"
+#colnames(X)[11]  <- "Conc"
+
+#set the OrfRep to YDL227C for the ref data
+X[X$ORF == "YDL227C",]$OrfRep <- "YDL227C"
+#Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. 
+#That is the first DM plates are removed from the data set with the following.
+#X <- X[X$Conc1 != "0ug/ml",] #This removes data with dox ==0 leaving gene expression on with four different concentrations of Gemcytabin
+X <- X[X$Drug != "BMH21",] #This removes data concerning BMH21 for this experiment
+
+#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. 
+#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used.
+#try(X <- X[X$ORF != "bad_spot",])
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=
+
+#get total number of drug concentrations
+Total_Conc_Nums <- length(unique(X$Conc))
+
+#function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
+numextract <- function(string){ 
+  str_extract(string, "\\-*\\d+\\.*\\d*")
+} 
+
+#generate a new column with the numeric drug concs
+X$Conc_Num <- as.numeric(numextract(X$Conc))
+#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
+X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1
+
+#Get the max factor for concentration
+MAX_CONC <- max(X$Conc_Num_Factor)
+#if treating numbers not as factors uncomment next line and comment out previous line
+#MAX_CONC <- max(X$Conc_Num)
+
+#remove wells with problems for making graphs and to not include in summary statistics
+X <- X[X$Gene != "BLANK",]
+X <- X[X$Gene != "Blank",] 
+X <- X[X$ORF != "Blank",]
+X <- X[X$Gene != "blank",]
+#X <- X[X$Gene != "HO",]
+Xbu= X
+#Inserted to use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script). This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
+#Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to write over the RF1&RF2 geneNames which caused a variance with his code results 
+#in the Z_lm_L,K,r&AUC output values. Values correlated well but were off by a multiplier factor.
+SGDgeneList= "../Code/SGD_features.tab"
+genes = data.frame(read.delim(file=SGDgeneList,quote="",header=FALSE,colClasses = c(rep("NULL",3), rep("character", 2), rep("NULL", 11))))
+for(i in 1:length(X[,14])){
+  ii= as.numeric(i)
+  line_num = match(X[ii,14],genes[,1],nomatch=1)
+  OrfRepColNum= as.numeric(match('OrfRep',names(X)))
+  if(X[ii,OrfRepColNum]!= "YDL227C"){
+     X[ii,15] = genes[line_num,2]
+  }
+  if((X[ii,15] == "")||(X[ii,15] == "OCT1")){
+    X[ii,15] = X[ii,OrfRepColNum]
+  }
+}
+Xblankreplace= X
+#X= Xbu #for restore testing restore X if geneName 'Match' routine needs changing
+
+#Remove dAmPs *******************************
+#jlh confirmed to leave dAmps in so comment out this section
+#DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
+#Damps <- read.delim(DAmPs_List,header=F)
+
+#X <- X[!(X$ORF %in% Damps$V1),]  #fix this to Damps[,1]
+#XafterDampsRM=X  #Backup for debugging especially when Rstudio goes crazy out of control
+# ***********
+
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++END USER DATA SELECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+print("ln137 End of User Section including blank gene writeOver")
+#++++Begin Graphics Boiler Plate Section+++++++++++++++++++++++++++++++++++++++
+#theme elements for plots
+theme_Publication <- function(base_size=14, base_family="sans") {
+  library(grid)
+  library(ggthemes)
+  (theme_foundation(base_size=base_size, base_family=base_family)
+    + theme(plot.title = element_text(face = "bold",
+                                      size = rel(1.2), hjust = 0.5),
+            text = element_text(),
+            panel.background = element_rect(colour = NA),
+            plot.background = element_rect(colour = NA),
+            panel.border = element_rect(colour = NA),
+            axis.title = element_text(face = "bold",size = rel(1)),
+            axis.title.y = element_text(angle=90,vjust =2),
+            axis.title.x = element_text(vjust = -0.2),
+            axis.text = element_text(), 
+            axis.line = element_line(colour="black"),
+            axis.ticks = element_line(),
+            panel.grid.major = element_line(colour="#f0f0f0"),
+            panel.grid.minor = element_blank(),
+            legend.key = element_rect(colour = NA),
+            legend.position = "bottom",
+            legend.direction = "horizontal",
+            legend.key.size= unit(0.2, "cm"),
+            legend.spacing = unit(0, "cm"),
+            legend.title = element_text(face="italic"),
+            plot.margin=unit(c(10,5,5,5),"mm"),
+            strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+            strip.text = element_text(face="bold")
+    ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  library(scales)
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
+  (theme_foundation(base_size=base_size, base_family=base_family)
+   + theme(plot.title = element_text(face = "bold",
+                                     size = rel(1.2), hjust = 0.5),
+           text = element_text(),
+           panel.background = element_rect(colour = NA),
+           plot.background = element_rect(colour = NA),
+           panel.border = element_rect(colour = NA),
+           axis.title = element_text(face = "bold",size = rel(1)),
+           axis.title.y = element_text(angle=90,vjust =2),
+           axis.title.x = element_text(vjust = -0.2),
+           axis.text = element_text(), 
+           axis.line = element_line(colour="black"),
+           axis.ticks = element_line(),
+           panel.grid.major = element_line(colour="#f0f0f0"),
+           panel.grid.minor = element_blank(),
+           legend.key = element_rect(colour = NA),
+           legend.position = "right",
+           legend.direction = "vertical",
+           legend.key.size= unit(0.5, "cm"),
+           legend.spacing = unit(0, "cm"),
+           legend.title = element_text(face="italic"),
+           plot.margin=unit(c(10,5,5,5),"mm"),
+           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+           strip.text = element_text(face="bold")
+   ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+#print timestamp for initial time the code starts
+timestamp()
+#+++++BEGIN QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+###Part 2 - Quality control
+#print quality control graphs for each dataset before removing data due to contamination
+#and before adjusting missing data to max theoretical values
+
+#plate analysis plot
+#plate analysis is a quality check to identify plate effects containing anomalies
+
+Plate_Analysis_L <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+
+
+Plate_Analysis_L_Box <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K_Box <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r_Box <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+#quality control - values with a high delta background likely have heavy contamination
+#check the frequency of these values
+#report the L and K values of these spots
+#report the number to be removed based on the Delta_Background_Tolerance
+X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd
+
+
+#raw l vs K before QC
+Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle("Raw L vs K before QC") +
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8)
+Raw_l_vs_K_beforeQC
+dev.off()
+pgg <- ggplotly(Raw_l_vs_K_beforeQC)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_beforeQC.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+
+#set delta background tolerance based on 3 sds from the mean delta background
+Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(DelBGFactr*sd(X$Delta_Backgrd))
+#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
+print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" "))
+
+Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+
+
+X_Delta_Backgrd_above_Tolerance <- X[X$Delta_Backgrd >= Delta_Background_Tolerance,]
+
+X_Delta_Backgrd_above_Tolerance_K_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$K,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_L_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$l,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_toRemove <- dim(X_Delta_Backgrd_above_Tolerance)[1]
+
+X_Delta_Backgrd_above_Tolerance_L_vs_K <- ggplot(X_Delta_Backgrd_above_Tolerance,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle(paste("Raw L vs K for strains above delta background threshold of",Delta_Background_Tolerance,"or above")) +
+  annotate("text",x=X_Delta_Backgrd_above_Tolerance_L_halfmedian,y=X_Delta_Backgrd_above_Tolerance_K_halfmedian,
+           label = paste("Strains above delta background tolerance = ",X_Delta_Backgrd_above_Tolerance_toRemove)) + 
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8)
+X_Delta_Backgrd_above_Tolerance_L_vs_K
+dev.off()
+pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+#frequency plot for all data vs. the delta_background
+DeltaBackground_Frequency_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_density() +
+  ggtitle("Density plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+#bar plot for all data vs. the delta_background
+DeltaBackground_Bar_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_bar() +
+  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+pdf(file = paste(outputpath_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8)
+print(DeltaBackground_Frequency_Plot)
+print(DeltaBackground_Bar_Plot)
+dev.off()
+
+
+#Need to identify missing data, and differentiate between this data and removed data so the removed data can get set to NA and the missing data can get set to max theoretical values
+#1 for missing data, 0 for non missing data
+#Use "NG" for NoGrowth rather than "missing"
+X$NG <- 0
+try(X[X$l == 0 & !is.na(X$l),]$NG <- 1)
+
+#1 for removed data, 0 non removed data
+#Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
+X$DB <- 0
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$DB <- 1)
+
+#replace the CPPs for l, r, AUC and K (must be last!) for removed data
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$l <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$r <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$AUC <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$K <- NA)
+
+
+Plate_Analysis_L_afterQC <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L
+Plate_Analysis_L_afterQC
+Plate_Analysis_K
+Plate_Analysis_K_afterQC
+Plate_Analysis_r
+Plate_Analysis_r_afterQC
+Plate_Analysis_AUC
+Plate_Analysis_AUC_afterQC
+Plate_Analysis_Delta_Backgrd
+Plate_Analysis_Delta_Backgrd_afterQC
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box
+Plate_Analysis_L_Box_afterQC
+Plate_Analysis_K_Box
+Plate_Analysis_K_Box_afterQC
+Plate_Analysis_r_Box
+Plate_Analysis_r_Box_afterQC
+Plate_Analysis_AUC_Box
+Plate_Analysis_AUC_Box_afterQC
+Plate_Analysis_Delta_Backgrd_Box
+Plate_Analysis_Delta_Backgrd_Box_afterQC
+dev.off()
+
+#remove the zero values and print plate analysis
+X_noZero <- X[which(X$l > 0),]
+Plate_Analysis_L_afterQC_Z <- ggplot(X_noZero,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC_Z <- ggplot(X_noZero,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC_Z <- ggplot(X_noZero,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC_Z <- ggplot(X_noZero,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC_Z <- ggplot(X_noZero,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L_afterQC_Z
+Plate_Analysis_K_afterQC_Z
+Plate_Analysis_r_afterQC_Z
+Plate_Analysis_AUC_afterQC_Z
+Plate_Analysis_Delta_Backgrd_afterQC_Z
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box_afterQC_Z
+Plate_Analysis_K_Box_afterQC_Z
+Plate_Analysis_r_Box_afterQC_Z
+Plate_Analysis_AUC_Box_afterQC_Z
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
+dev.off()
+
+#remove dataset with zeros removed
+rm(X_noZero)
+
+
+#X_test_missing_and_removed <- X[X$Removed == 1,]
+
+#calculate summary statistics for all strains, including both background and the deletions
+X_stats_ALL <- ddply(X, c("Conc_Num","Conc_Num_Factor"), summarise,
+                     N = (length(l)),
+                     mean_L = mean(l,na.rm=TRUE),
+                     median_L = median(l,na.rm=TRUE),
+                     max_L = max(l,na.rm=TRUE),
+                     min_L = min(l,na.rm=TRUE),
+                     sd_L = sd(l,na.rm=TRUE),
+                     se_L = sd_L / sqrt(N-1),
+                     mean_K = mean(K,na.rm=TRUE),
+                     median_K = median(K,na.rm=TRUE),
+                     max_K = max(K,na.rm=TRUE),
+                     min_K = min(K,na.rm=TRUE),
+                     sd_K = sd(K,na.rm=TRUE),
+                     se_K = sd_K / sqrt(N-1),
+                     mean_r = mean(r,na.rm=TRUE),
+                     median_r = median(r,na.rm=TRUE),
+                     max_r = max(r,na.rm=TRUE),
+                     min_r = min(r,na.rm=TRUE),
+                     sd_r = sd(r,na.rm=TRUE),
+                     se_r = sd_r / sqrt(N-1),
+                     mean_AUC = mean(AUC,na.rm=TRUE),
+                     median_AUC = median(AUC,na.rm=TRUE),
+                     max_AUC = max(AUC,na.rm=TRUE),
+                     min_AUC = min(AUC,na.rm=TRUE),
+                     sd_AUC = sd(AUC,na.rm=TRUE),
+                     se_AUC = sd_AUC / sqrt(N-1)
+)
+#print(X_stats_ALL_L)
+
+write.csv(X_stats_ALL,file=paste(outputpath,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE)
+#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+##### Part 3 - Generate summary statistics and calculate the max theoretical L value
+##### Calculate the Z score at each drug conc for each deletion strain
+
+
+#get the background strains - can be modified to take another argument but for most screens will just be YDL227C
+Background_Strains <- c("YDL227C")
+
+#first part of loop will go through for each background strain 
+#most cases there will only be one YDL227C
+for(s in Background_Strains){
+  X_Background <- X[X$OrfRep == s,]
+  
+  #if there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
+  #we may want to consider in some cases giving the max high value to L depending on the data type
+  if(table(X_Background$l)[1] == 0){
+    X_Background[X_Background$l == 0,]$l <- NA
+    X_Background[X_Background$K == 0,]$K <- NA
+    X_Background[X_Background$r == 0,]$r <- NA
+    X_Background[X_Background$AUC == 0,]$AUC <- NA
+  }
+  
+  X_Background <- X_Background[!is.na(X_Background$l),]
+  
+  #get summary stats for L, K, R, AUC
+  X_stats_BY_L <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(l)),
+                        mean = mean(l,na.rm=TRUE),
+                        median = median(l,na.rm=TRUE),
+                        max = max(l,na.rm=TRUE),
+                        min = min(l,na.rm=TRUE),
+                        sd = sd(l,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L)
+  X1_SD <- max(X_stats_BY_L$sd)
+  
+  X_stats_BY_K <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(K)),
+                        mean = mean(K,na.rm=TRUE),
+                        median = median(K,na.rm=TRUE),
+                        max = max(K,na.rm=TRUE),
+                        min = min(K,na.rm=TRUE),
+                        sd = sd(K,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_K <- max(X_stats_BY_K$sd)
+  
+  
+  X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = length(r),
+                        mean = mean(r,na.rm=TRUE),
+                        median = median(r,na.rm=TRUE),
+                        max = max(r,na.rm=TRUE),
+                        min = min(r,na.rm=TRUE),
+                        sd = sd(r,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_r <- max(X_stats_BY_r$sd)
+  
+  X_stats_BY_AUC <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                          N = length(AUC),
+                          mean = mean(AUC,na.rm=TRUE),
+                          median = median(AUC,na.rm=TRUE),
+                          max = max(AUC,na.rm=TRUE),
+                          min = min(AUC,na.rm=TRUE),
+                          sd = sd(AUC,na.rm=TRUE),
+                          se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
+  
+  X_stats_BY <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                      N = (length(l)),
+                      mean_L = mean(l,na.rm=TRUE),
+                      median_L = median(l,na.rm=TRUE),
+                      max_L = max(l,na.rm=TRUE),
+                      min_L = min(l,na.rm=TRUE),
+                      sd_L = sd(l,na.rm=TRUE),
+                      se_L = sd_L / sqrt(N-1),
+                      mean_K = mean(K,na.rm=TRUE),
+                      median_K = median(K,na.rm=TRUE),
+                      max_K = max(K,na.rm=TRUE),
+                      min_K = min(K,na.rm=TRUE),
+                      sd_K = sd(K,na.rm=TRUE),
+                      se_K = sd_K / sqrt(N-1),
+                      mean_r = mean(r,na.rm=TRUE),
+                      median_r = median(r,na.rm=TRUE),
+                      max_r = max(r,na.rm=TRUE),
+                      min_r = min(r,na.rm=TRUE),
+                      sd_r = sd(r,na.rm=TRUE),
+                      se_r = sd_r / sqrt(N-1),
+                      mean_AUC = mean(AUC,na.rm=TRUE),
+                      median_AUC = median(AUC,na.rm=TRUE),
+                      max_AUC = max(AUC,na.rm=TRUE),
+                      min_L = min(AUC,na.rm=TRUE),
+                      sd_AUC = sd(AUC,na.rm=TRUE),
+                      se_AUC = sd_AUC / sqrt(N-1)
+  )
+  
+  write.csv(X_stats_BY,file=paste(outputpath,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE)
+  
+  #calculate the max theoretical L values
+  #only look for max values when K is within 2SD of the ref strain
+  for(q in unique(X$Conc_Num_Factor)){
+    if(q == 0){
+      X_within_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[!is.na(X_within_2SD_K$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) ,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+      X_outside_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[!is.na(X_outside_2SD_K$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1]))  ,]
+      #X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+    }
+    if(q > 0){
+      X_within_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K <- rbind(X_within_2SD_K,X_within_2SD_K_temp)
+      
+      X_outside_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      #X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      X_outside_2SD_K <- rbind(X_outside_2SD_K,X_outside_2SD_K_temp)
+    }
+  }
+  
+  X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                     N = (length(l)),
+                                     mean = mean(l),
+                                     median = median(l),
+                                     max = max(l,na.rm=TRUE),
+                                     min = min(l,na.rm=TRUE),
+                                     sd = sd(l),
+                                     se = sd / sqrt(N-1),
+                                     z_max = (max-mean)/sd
+  )
+  print(X_stats_BY_L_within_2SD_K)
+  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)
+  write.csv(X_stats_BY_L_within_2SD_K,file=paste(outputpath_QC,"Max_Observed_L_Vals_for_spots_within_2SD_K.csv",sep=""),row.names=FALSE)
+  
+  X_stats_BY_L_outside_2SD_K <- ddply(X_outside_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                      N = (length(l)),
+                                      mean = mean(l),
+                                      median = median(l),
+                                      max = max(l,na.rm=TRUE),
+                                      min = min(l,na.rm=TRUE),
+                                      sd = sd(l),
+                                      se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L_outside_2SD_K)
+  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)
+  
+  #X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l,]
+  Outside_2SD_K_L_vs_K <- ggplot(X_outside_2SD_K,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  print(Outside_2SD_K_L_vs_K)
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"RawL_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  Outside_2SD_K_delta_background_vs_K <- ggplot(X_outside_2SD_K,aes(Delta_Backgrd,K,color=as.factor(Conc_Num))) + geom_point(aes(l=l,ORF=ORF,Gene=Gene),shape=3,position="jitter") +
+    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  Outside_2SD_K_delta_background_vs_K
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"DeltaBackground_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  #get the background strain mean values at the no drug conc to calculate shift
+  Background_L <- X_stats_BY_L$mean[1]
+  Background_K <- X_stats_BY_K$mean[1]
+  Background_r <- X_stats_BY_r$mean[1]
+  Background_AUC <- X_stats_BY_AUC$mean[1]
+  
+  #create empty plots for plotting element
+  p_l <-  ggplot()
+  p_K <-  ggplot()
+  p_r <-  ggplot()
+  p_AUC <-  ggplot()
+  
+  p_rf_l <-  ggplot()
+  p_rf_K <-  ggplot()
+  p_rf_r <-  ggplot()
+  p_rf_AUC <-  ggplot()
+  
+  #get only the deletion strains
+  X2 <- X
+  X2 <- X2[X2$OrfRep != "YDL227C",]
+  
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2$Conc_Num))){
+    Concentration <- unique(X2$Conc_Num)[i]
+    X2_temp <- X2[X2$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_new <- X2_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      
+      #X2_temp[X2_temp$K == 0,]$K <- X_stats_ALL_K$max[i]
+      #X2_temp[X2_temp$r == 0,]$r <- X_stats_ALL_r$max[i]
+      #X2_temp[X2_temp$AUC == 0,]$AUC <- X_stats_ALL_AUC$max[i]
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+      
+      X2_new <- rbind(X2_new,X2_temp)
+      
+    }
+  }
+  X2 <- X2_new
+  
+  
+  #get only the RF strains
+  X2_RF <- X
+  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",]
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2_RF$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2_RF$Conc_Num))){
+    Concentration <- unique(X2_RF$Conc_Num)[i]
+    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_RF_new <- X2_RF_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      print(paste("Check loop order, if error, refs have no L values outside theoretical max L, for REFs, conc =",Concentration,sep=" "))
+      
+      X2_RF_new <- rbind(X2_RF_new,X2_RF_temp)
+      
+    }
+  }
+  X2_RF <- X2_RF_new
+  
+  
+  #######Part 4 Get the RF Z score values
+  #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
+  X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_")
+  
+  num_genes_RF <- length(unique(X2_RF$OrfRep))
+  print(num_genes_RF)
+  
+  
+  #create the output data.frame containing columns for each RF strain
+  InteractionScores_RF <- unique(X2_RF["OrfRep"])
+  #InteractionScores_RF$Gene <- unique(X2$Gene)
+  InteractionScores_RF$Gene <- NA
+  InteractionScores_RF$Raw_Shift_L <- NA
+  InteractionScores_RF$Z_Shift_L <- NA
+  InteractionScores_RF$lm_Score_L <- NA
+  InteractionScores_RF$Z_lm_L <- NA  
+  InteractionScores_RF$R_Squared_L <- NA
+  InteractionScores_RF$Sum_Z_Score_L <- NA
+  InteractionScores_RF$Avg_Zscore_L <- NA
+  InteractionScores_RF$Raw_Shift_K <- NA
+  InteractionScores_RF$Z_Shift_K <- NA
+  InteractionScores_RF$lm_Score_K <- NA
+  InteractionScores_RF$Z_lm_K <- NA  
+  InteractionScores_RF$R_Squared_K <- NA
+  InteractionScores_RF$Sum_Z_Score_K <- NA
+  InteractionScores_RF$Avg_Zscore_K <- NA
+  InteractionScores_RF$Raw_Shift_r <- NA
+  InteractionScores_RF$Z_Shift_r <- NA
+  InteractionScores_RF$lm_Score_r <- NA
+  InteractionScores_RF$Z_lm_r <- NA
+  InteractionScores_RF$R_Squared_r <- NA
+  InteractionScores_RF$Sum_Z_Score_r <- NA
+  InteractionScores_RF$Avg_Zscore_r <- NA
+  InteractionScores_RF$Raw_Shift_AUC <- NA
+  InteractionScores_RF$Z_Shift_AUC <- NA
+  InteractionScores_RF$lm_Score_AUC <- NA
+  InteractionScores_RF$Z_lm_AUC <- NA
+  InteractionScores_RF$R_Squared_AUC <- NA
+  InteractionScores_RF$Sum_Z_Score_AUC <- NA
+  InteractionScores_RF$Avg_Zscore_AUC <- NA
+  InteractionScores_RF$NG <- NA
+  InteractionScores_RF$SM <- NA
+  
+  
+  for(i in 1:num_genes_RF){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2_RF$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      gene_lm_AUC <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      gene_interaction_AUC <- NA
+      r_squared_AUC <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction)
+    }
+    
+    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass RF Calculation loop")
+  
+  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  print(paste("Mean RF linear regression score L",lm_mean_L))
+  
+  
+  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L)/(lm_sd_L)
+  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K)/(lm_sd_K)
+  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r)/(lm_sd_r)
+  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC)/(lm_sd_AUC)
+  
+  
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L,decreasing=TRUE),]
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG,decreasing=TRUE),]
+  write.csv(InteractionScores_RF,paste(outputpath,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  
+  for(i in 1:num_genes_RF){
+    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel,]
+    
+    p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg Zscore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm Zscore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_rf_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final,X_ZCalculations)
+    }
+  }
+  print("Pass RF ggplot loop")
+  write.csv(X_stats_interaction_ALL_RF_final,paste(outputpath,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  ####### Part 5 - Get Zscores for Gene deletion strains
+  
+  
+  
+  #get total number of genes for the next loop
+  num_genes <- length(unique(X2$OrfRep))
+  print(num_genes)
+  
+  #create the output data.frame containing columns for each deletion strain
+  InteractionScores <- unique(X2["OrfRep"])
+  #InteractionScores$Gene <- unique(X2$Gene)
+  InteractionScores$Gene <- NA
+  InteractionScores$Raw_Shift_L <- NA
+  InteractionScores$Z_Shift_L <- NA
+  InteractionScores$lm_Score_L <- NA
+  InteractionScores$Z_lm_L <- NA
+  InteractionScores$R_Squared_L <- NA
+  InteractionScores$Sum_Z_Score_L <- NA
+  InteractionScores$Avg_Zscore_L <- NA
+  InteractionScores$Raw_Shift_K <- NA
+  InteractionScores$Z_Shift_K <- NA
+  InteractionScores$lm_Score_K <- NA
+  InteractionScores$Z_lm_K <- NA
+  InteractionScores$R_Squared_K <- NA
+  InteractionScores$Sum_Z_Score_K <- NA
+  InteractionScores$Avg_Zscore_K <- NA
+  InteractionScores$Raw_Shift_r <- NA
+  InteractionScores$Z_Shift_r <- NA
+  InteractionScores$lm_Score_r <- NA
+  InteractionScores$Z_lm_r <- NA
+  InteractionScores$R_Squared_r <- NA
+  InteractionScores$Sum_Z_Score_r <- NA
+  InteractionScores$Avg_Zscore_r <- NA
+  InteractionScores$Raw_Shift_AUC <- NA
+  InteractionScores$Z_Shift_AUC <- NA
+  InteractionScores$lm_Score_AUC <- NA
+  InteractionScores$Z_lm_AUC <- NA
+  InteractionScores$R_Squared_AUC <- NA
+  InteractionScores$Sum_Z_Score_AUC <- NA
+  InteractionScores$Avg_Zscore_AUC <- NA
+  InteractionScores$NG <- NA
+  InteractionScores$DB <- NA
+  InteractionScores$SM <- NA
+  
+  for(i in 1:num_genes){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_L-lm_mean_L)/lm_sd_L
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_K-lm_mean_K)/lm_sd_K
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_r-lm_mean_r)/lm_sd_r
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_AUC-lm_mean_AUC)/lm_sd_AUC
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-NA
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction)
+    }
+    
+    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass Int Calculation loop")
+  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L,decreasing=TRUE),]
+  InteractionScores <- InteractionScores[order(InteractionScores$NG,decreasing=TRUE),]
+  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
+  #X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG,decreasing=TRUE),]
+  write.csv(InteractionScores,paste(outputpath,"ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_K <- InteractionScores[InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores_deletion_enhancers_L[!is.na(InteractionScores_deletion_enhancers_L$OrfRep),]
+  InteractionScores_deletion_enhancers_K <- InteractionScores_deletion_enhancers_K[!is.na(InteractionScores_deletion_enhancers_K$OrfRep),]
+  InteractionScores_deletion_suppressors_L <- InteractionScores_deletion_suppressors_L[!is.na(InteractionScores_deletion_suppressors_L$OrfRep),]
+  InteractionScores_deletion_suppressors_K <- InteractionScores_deletion_suppressors_K[!is.na(InteractionScores_deletion_suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores_deletion_enhancers_and_Suppressors_L[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores_deletion_enhancers_and_Suppressors_K[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE)
+  
+  #get enhancers and suppressors for linear regression
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2,]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores[InteractionScores$Z_lm_K <= -2,]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores_deletion_enhancers_L_lm[!is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores_deletion_enhancers_K_lm[!is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores_deletion_suppressors_L_lm[!is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores_deletion_suppressors_K_lm[!is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores_deletion_enhancers_and_Suppressors_L_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores_deletion_enhancers_and_Suppressors_K_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE)
+  
+  
+  write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+  print('ln 1570 write StudyInfo.csv after ')
+        #write.table(Labels,file=paste("../Code/StudyInfo.txt"),sep="\t",row.names = FALSE)
+  
+  for(i in 1:num_genes){
+    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel,]
+    
+    p_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_K,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final,X_ZCalculations)
+    }
+  }
+  print("Pass Int ggplot loop")
+  write.csv(X_stats_interaction_ALL_final,paste(outputpath,"ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  
+  
+  
+  
+  Blank <- ggplot(X2_RF) + geom_blank()
+  
+  pdf(paste(outputpath,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_l[[num]],p_K[[num]],p_r[[num]],p_AUC[[num]],p_l[[num+1]],p_K[[num+1]],p_r[[num+1]],p_AUC[[num+1]],p_l[[num+2]],p_K[[num+2]],p_r[[num+2]],p_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_l[[364]],p_K[[364]],p_r[[364]],p_l[[365]],p_K[[365]],p_r[[365]],p_l[[366]],p_K[[366]],p_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_K[[num+3]],p_r[[num+3]],p1[[num+4]],p_K[[num+4]],p_r[[num+4]]
+  }
+  if(num_genes != (num+2)){
+    total_num = num_genes - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],p_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  
+  
+  pdf(paste(outputpath,"RF_InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(12000,0)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes_RF)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes_RF)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_rf_l[[num]],p_rf_K[[num]],p_rf_r[[num]],p_rf_AUC[[num]],p_rf_l[[num+1]],p_rf_K[[num+1]],p_rf_r[[num+1]],p_rf_AUC[[num+1]],p_rf_l[[num+2]],p_rf_K[[num+2]],p_rf_r[[num+2]],p_rf_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_rf_l[[364]],p_rf_K[[364]],p_rf_r[[364]],p_rf_l[[365]],p_rf_K[[365]],p_rf_r[[365]],p_rf_l[[366]],p_rf_K[[366]],p_rf_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p1[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]]
+  }
+  if(num_genes_RF != (num+2)){
+    total_num = num_genes_RF - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],p_rf_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  #print rank plots for L and K gene interactions
+  
+  
+  InteractionScores_AdjustMissing <- InteractionScores
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L),]$Avg_Zscore_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K),]$Avg_Zscore_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r),]$Avg_Zscore_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC),]$Avg_Zscore_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank <- NA
+  InteractionScores_AdjustMissing$K_Rank <- NA
+  InteractionScores_AdjustMissing$r_Rank <- NA
+  InteractionScores_AdjustMissing$AUC_Rank <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
+  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
+  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
+  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
+  
+  #
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L),]$Z_lm_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K),]$Z_lm_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r),]$Z_lm_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC),]$Z_lm_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- NA
+  InteractionScores_AdjustMissing$K_Rank_lm <- NA
+  InteractionScores_AdjustMissing$r_Rank_lm <- NA
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
+  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
+  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
+  
+  
+  
+  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  
+  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,]
+  
+  #find overlaps
+  X_NArm$Overlap <- "No Effect"
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Both")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Both")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2,]$Overlap <- "Deletion Enhancer lm only")
+  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2,]$Overlap <- "Deletion Suppressor lm only")
+  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L <- lm(X_NArm$Z_lm_L~X_NArm$Avg_Zscore_L)
+  L_lm <- summary(get_lm_L)
+  
+  get_lm_K <- lm(X_NArm$Z_lm_K~X_NArm$Avg_Zscore_K)
+  K_lm <- summary(get_lm_K)
+  
+  get_lm_r <- lm(X_NArm$Z_lm_r~X_NArm$Avg_Zscore_r)
+  r_lm <- summary(get_lm_r)
+  
+  get_lm_AUC <- lm(X_NArm$Z_lm_AUC~X_NArm$Avg_Zscore_AUC)
+  AUC_lm <- summary(get_lm_AUC)
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm L") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_K,Z_lm_K)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm K") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(K_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_r,Z_lm_r)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm r") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(r_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_AUC,Z_lm_AUC)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm AUC") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(AUC_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  dev.off()
+  
+  
+  lm_v_Zscore_L <- ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L,ORF=OrfRep,Gene=Gene,NG=NG,SM=SM,DB=DB)) + geom_point(aes(color=Overlap),shape=3) + 
+    geom_smooth(method = "lm",color=1) +  ggtitle("Avg Zscore vs lm L") +
+    geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+    annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right()
+  
+  pgg <- ggplotly(lm_v_Zscore_L)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath,"Avg_Zscore_vs_lm_NA_rm.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
+  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
+  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
+  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
+  
+  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
+  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
+  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
+  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L2 <- lm(X_NArm$L_Rank_lm~X_NArm$L_Rank)
+  L_lm2 <- summary(get_lm_L2)
+  
+  get_lm_K2 <- lm(X_NArm$K_Rank_lm~X_NArm$K_Rank)
+  K_lm2 <- summary(get_lm_K2)
+  
+  get_lm_r2 <- lm(X_NArm$r_Rank_lm~X_NArm$r_Rank)
+  r_lm2 <- summary(get_lm_r2)
+  
+  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm~X_NArm$AUC_Rank)
+  AUC_lm2 <- summary(get_lm_AUC2)
+  
+  num_genes_NArm2 <- (dim(X_NArm)[1])/2
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_ranked_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm L") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(K_Rank,K_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm K") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(K_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(r_Rank,r_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm r") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(r_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(AUC_Rank,AUC_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank of Avg Zscore vs lm AUC") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(AUC_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  
+  
+  dev.off()
+  
+  
+  
+  Rank_L_1SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+}
+
+
+
+#get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+get_lm_1 <- lm(X_NArm$Z_lm_K~X_NArm$Z_lm_L)
+L_lm_1 <- summary(get_lm_1)
+
+get_lm_2 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_L)
+L_lm_2 <- summary(get_lm_2)
+
+get_lm_3 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_L)
+L_lm_3 <- summary(get_lm_3)
+
+get_lm_4 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_K)
+L_lm_4 <- summary(get_lm_4)
+
+get_lm_5 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_K)
+L_lm_5 <- summary(get_lm_5)
+
+get_lm_6 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_r)
+L_lm_6 <- summary(get_lm_6)
+
+
+pdf(file=paste(outputpath,"Correlation_CPPs.pdf",sep=""),width = 10, height = 7, onefile = TRUE)
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L),]
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_K),color="cyan") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_r),color="cyan") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_r),color="cyan") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_r,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+
+
+
+
+dev.off()
+
+
+#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE)
+timestamp()
+
+#BoneYard***********************************************
+#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced.
+#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. 
+#Corruptable but better than nothing.
+#if(is.na(Args[2])){
+# std=3
+#}else {
+# std= Arg[2]
+#Delta_Background_sdFactor <- 2  #Args[3]
+#DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#} 
+
diff --git a/workflow/.old/templates/qhtcp/Exp4/backups/InteractTemplateB4Prompt4SDinput.R b/workflow/.old/templates/qhtcp/Exp4/backups/InteractTemplateB4Prompt4SDinput.R
new file mode 100644
index 00000000..3b11777c
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/Exp4/backups/InteractTemplateB4Prompt4SDinput.R
@@ -0,0 +1,2702 @@
+#Based on InteractionTemplate.R which is based on Sean Santose's Interaction_V5 script.
+#Adapt SS For Structured Data storage but using command line scripts
+###Set up the required libraries, call required plot theme elements and set up the command line arguments
+library("ggplot2")
+library("plyr")
+library("extrafont")
+library("gridExtra")
+library("gplots")
+library("RColorBrewer")
+library("stringr")
+#library("gdata")
+library(plotly)
+library(htmlwidgets)
+
+Args <- commandArgs(TRUE)
+input_file <- Args[1] #"!!Results_17_0827_yor1null-rpl12anull misLabeledAsFrom MI 17_0919_yor1-curated.txt"  #Args[1]  #Arg 1 #"!!ResultsStd_JS 19_1224_HLEG_P53.txt" is the !!results ... .txt
+
+#Path to Output Directory
+W=getwd() #R is F'd up, Can't use, Any legitamate platfold could build out dirs from this
+outDir <- "ZScores/" #"Args[2] #paste0(W,"/ZScores/")
+subDir <- outDir     #Args[2]
+
+if (file.exists(subDir)){
+  outputpath <- subDir
+} else {
+  dir.create(file.path(subDir))
+}
+
+if (file.exists(paste(subDir,"QC/",sep=""))){
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+} else {
+  dir.create(file.path(paste(subDir,"QC/",sep="")))
+  outputpath_QC <- paste(subDir,"QC/",sep="")
+}
+#define the output path (formerly the second argument from Rscript)
+outputpath <- outDir 
+
+#Set Args[2] the Background contamination noise filter as a function of standard deviation
+#Sean recommends 3 or 5 SD factor.
+#Capture Exp_ number,use it to Save Args[2]{std}to Labels field and then Write to Labels to studyInfo.txt for future reference
+Labels <- read.csv(file= "../Code/StudyInfo.csv",stringsAsFactors = FALSE)  #,sep= ",")
+print("Be sure to include Argument 2 the Bacground noise filter standard deviation i.e., 3 or 5 per Sean")
+std= as.numeric(Args[2])
+expNumber<- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd()))
+Labels[expNumber,3]= as.numeric(std)
+Delta_Background_sdFactor <- std
+DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#Write Background SD value to studyInfo.txt file
+#write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+print('ln 50 write StudyInfo.csv ')
+#write.table(Labels,file=paste(outputpath,"StudyInfo.txt"),sep = "\t",row.names = FALSE)
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++BEGIN USER DATA SELECTION SECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+#read in the data
+X <- read.delim(input_file,skip=2,as.is=T,row.names=1,strip.white=TRUE)
+X <- X[!(X[[1]]%in%c("","Scan")),]
+#X <- X[!(X[[1]]%in%c(61:76)),]  #Remove dAmp plates which are Scans 61 thru 76
+
+#X <- X[which(X$Specifics == "WT"),]
+
+X_length <- length(X[1,])
+X_end <- length(X[1,]) - 2
+X <- X[,c(1:46,X_end:X_length)]
+
+
+#use numeric data to perform operations
+X$Col <- as.numeric(X$Col)
+X$Row <- as.numeric(X$Row)
+X$l <- as.numeric(X$l)
+X$K <- as.numeric(X$K)
+X$r <- as.numeric(X$r)
+X$Scan <- as.numeric(X$Scan)
+X$AUC <- as.numeric(X$AUC)
+X$LstBackgrd <- as.numeric(X$LstBackgrd)
+X$X1stBackgrd <- as.numeric(X$X1stBackgrd)
+
+#set the OrfRep to YDL227C for the ref data
+X[X$ORF == "YDL227C",]$OrfRep <- "YDL227C"
+#Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. 
+#That is the first DM plates are removed from the data set with the following.
+X <- X[X$Conc1 != "0ug/mL",] #This occurs only for Exp1 and Exp2 and so doesn't have any effect on Exp3&4
+
+
+#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. 
+#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used.
+#try(X <- X[X$ORF != "bad_spot",])
+#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=
+
+#get total number of drug concentrations
+Total_Conc_Nums <- length(unique(X$Conc))
+
+#function to ID numbers in string with characters+numbers (ie to get numeric drug conc)
+numextract <- function(string){ 
+  str_extract(string, "\\-*\\d+\\.*\\d*")
+} 
+
+#generate a new column with the numeric drug concs
+X$Conc_Num <- as.numeric(numextract(X$Conc))
+#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later
+X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1
+
+#Get the max factor for concentration
+MAX_CONC <- max(X$Conc_Num_Factor)
+#if treating numbers not as factors uncomment next line and comment out previous line
+#MAX_CONC <- max(X$Conc_Num)
+
+#remove wells with problems for making graphs and to not include in summary statistics
+X <- X[X$Gene != "BLANK",]
+X <- X[X$Gene != "Blank",] 
+X <- X[X$ORF != "Blank",]
+X <- X[X$Gene != "blank",]
+#X <- X[X$Gene != "HO",]
+Xbu= X
+#Inserted to use SGDgenelist to update orfs and replace empty geneName cells with ORF name (adapted from Sean's Merge script). This is to 'fix' the naming for everything that follows (REMc, Heatmaps ... et.al) rather than do it piece meal later
+#Sean's Match Script( which was adapted here) was fixed 2022_0608 so as not to write over the RF1&RF2 geneNames which caused a variance with his code results 
+#in the Z_lm_L,K,r&AUC output values. Values correlated well but were off by a multiplier factor.
+SGDgeneList= "../Code/SGD_features.tab"
+genes = data.frame(read.delim(file=SGDgeneList,quote="",header=FALSE,colClasses = c(rep("NULL",3), rep("character", 2), rep("NULL", 11))))
+for(i in 1:length(X[,14])){
+  ii= as.numeric(i)
+  line_num = match(X[ii,14],genes[,1],nomatch=1)
+  OrfRepColNum= as.numeric(match('OrfRep',names(X)))
+  if(X[ii,OrfRepColNum]!= "YDL227C"){
+     X[ii,15] = genes[line_num,2]
+  }
+  if((X[ii,15] == "")||(X[ii,15] == "OCT1")){
+    X[ii,15] = X[ii,OrfRepColNum]
+  }
+}
+Xblankreplace= X
+#X= Xbu #for restore testing restore X if geneName 'Match' routine needs changing
+
+#Remove dAmPs *******************************
+DAmPs_List <- "../Code/22_0602_Remy_DAmPsList.txt"
+Damps <- read.delim(DAmPs_List,header=F)
+
+X <- X[!(X$ORF %in% Damps$V1),]  #fix this to Damps[,1]
+XafterDampsRM=X  #Backup for debugging especially when Rstudio goes crazy out of control
+# ***********
+
+
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++END USER DATA SELECTION+++++++++++++++++++++++++++++++++++++++++++++++++
+#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+print("ln137 End of User Section including blank gene writeOver")
+#++++Begin Graphics Boiler Plate Section+++++++++++++++++++++++++++++++++++++++
+#theme elements for plots
+theme_Publication <- function(base_size=14, base_family="sans") {
+  library(grid)
+  library(ggthemes)
+  (theme_foundation(base_size=base_size, base_family=base_family)
+    + theme(plot.title = element_text(face = "bold",
+                                      size = rel(1.2), hjust = 0.5),
+            text = element_text(),
+            panel.background = element_rect(colour = NA),
+            plot.background = element_rect(colour = NA),
+            panel.border = element_rect(colour = NA),
+            axis.title = element_text(face = "bold",size = rel(1)),
+            axis.title.y = element_text(angle=90,vjust =2),
+            axis.title.x = element_text(vjust = -0.2),
+            axis.text = element_text(), 
+            axis.line = element_line(colour="black"),
+            axis.ticks = element_line(),
+            panel.grid.major = element_line(colour="#f0f0f0"),
+            panel.grid.minor = element_blank(),
+            legend.key = element_rect(colour = NA),
+            legend.position = "bottom",
+            legend.direction = "horizontal",
+            legend.key.size= unit(0.2, "cm"),
+            legend.spacing = unit(0, "cm"),
+            legend.title = element_text(face="italic"),
+            plot.margin=unit(c(10,5,5,5),"mm"),
+            strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+            strip.text = element_text(face="bold")
+    ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  library(scales)
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+theme_Publication_legend_right <- function(base_size=14, base_family="sans") {
+  (theme_foundation(base_size=base_size, base_family=base_family)
+   + theme(plot.title = element_text(face = "bold",
+                                     size = rel(1.2), hjust = 0.5),
+           text = element_text(),
+           panel.background = element_rect(colour = NA),
+           plot.background = element_rect(colour = NA),
+           panel.border = element_rect(colour = NA),
+           axis.title = element_text(face = "bold",size = rel(1)),
+           axis.title.y = element_text(angle=90,vjust =2),
+           axis.title.x = element_text(vjust = -0.2),
+           axis.text = element_text(), 
+           axis.line = element_line(colour="black"),
+           axis.ticks = element_line(),
+           panel.grid.major = element_line(colour="#f0f0f0"),
+           panel.grid.minor = element_blank(),
+           legend.key = element_rect(colour = NA),
+           legend.position = "right",
+           legend.direction = "vertical",
+           legend.key.size= unit(0.5, "cm"),
+           legend.spacing = unit(0, "cm"),
+           legend.title = element_text(face="italic"),
+           plot.margin=unit(c(10,5,5,5),"mm"),
+           strip.background=element_rect(colour="#f0f0f0",fill="#f0f0f0"),
+           strip.text = element_text(face="bold")
+   ))
+  
+}
+
+scale_fill_Publication <- function(...){
+  discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+scale_colour_Publication <- function(...){
+  discrete_scale("colour","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...)
+  
+}
+
+
+#print timestamp for initial time the code starts
+timestamp()
+#+++++BEGIN QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+###Part 2 - Quality control
+#print quality control graphs for each dataset before removing data due to contamination
+#and before adjusting missing data to max theoretical values
+
+#plate analysis plot
+#plate analysis is a quality check to identify plate effects containing anomalies
+
+Plate_Analysis_L <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+
+
+Plate_Analysis_L_Box <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L before quality control") + theme_Publication()
+
+Plate_Analysis_K_Box <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K before quality control") + theme_Publication()
+
+Plate_Analysis_r_Box <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r before quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC before quality control") + theme_Publication()
+
+#quality control - values with a high delta background likely have heavy contamination
+#check the frequency of these values
+#report the L and K values of these spots
+#report the number to be removed based on the Delta_Background_Tolerance
+X$Delta_Backgrd <- X$LstBackgrd - X$X1stBackgrd
+
+
+#raw l vs K before QC
+Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle("Raw L vs K before QC") +
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8)
+Raw_l_vs_K_beforeQC
+dev.off()
+pgg <- ggplotly(Raw_l_vs_K_beforeQC)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_beforeQC.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+
+#set delta background tolerance based on 3 sds from the mean delta background
+Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(DelBGFactr*sd(X$Delta_Backgrd))
+#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd))
+print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" "))
+
+Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication()
+
+
+
+X_Delta_Backgrd_above_Tolerance <- X[X$Delta_Backgrd >= Delta_Background_Tolerance,]
+
+X_Delta_Backgrd_above_Tolerance_K_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$K,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_L_halfmedian <- (median(X_Delta_Backgrd_above_Tolerance$l,na.rm = TRUE))/2
+X_Delta_Backgrd_above_Tolerance_toRemove <- dim(X_Delta_Backgrd_above_Tolerance)[1]
+
+X_Delta_Backgrd_above_Tolerance_L_vs_K <- ggplot(X_Delta_Backgrd_above_Tolerance,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+  ggtitle(paste("Raw L vs K for strains above delta background threshold of",Delta_Background_Tolerance,"or above")) +
+  annotate("text",x=X_Delta_Backgrd_above_Tolerance_L_halfmedian,y=X_Delta_Backgrd_above_Tolerance_K_halfmedian,
+           label = paste("Strains above delta background tolerance = ",X_Delta_Backgrd_above_Tolerance_toRemove)) + 
+  theme_Publication_legend_right()
+pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8)
+X_Delta_Backgrd_above_Tolerance_L_vs_K
+dev.off()
+pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
+#pgg
+plotly_path <- paste(getwd(),"/",outputpath_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="")
+saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+
+#frequency plot for all data vs. the delta_background
+DeltaBackground_Frequency_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_density() +
+  ggtitle("Density plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+#bar plot for all data vs. the delta_background
+DeltaBackground_Bar_Plot <- ggplot(X,aes(Delta_Backgrd,color=as.factor(Conc_Num))) + geom_bar() +
+  ggtitle("Bar plot for Delta Background by Conc All Data") + theme_Publication_legend_right()
+
+pdf(file = paste(outputpath_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8)
+print(DeltaBackground_Frequency_Plot)
+print(DeltaBackground_Bar_Plot)
+dev.off()
+
+
+#Need to identify missing data, and differentiate between this data and removed data so the removed data can get set to NA and the missing data can get set to max theoretical values
+#1 for missing data, 0 for non missing data
+#Use "NG" for NoGrowth rather than "missing"
+X$NG <- 0
+try(X[X$l == 0 & !is.na(X$l),]$NG <- 1)
+
+#1 for removed data, 0 non removed data
+#Use DB to identify number of genes removed due to the DeltaBackground Threshold rather than "Removed"
+X$DB <- 0
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$DB <- 1)
+
+#replace the CPPs for l, r, AUC and K (must be last!) for removed data
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$l <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$r <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$AUC <- NA)
+try(X[X$Delta_Backgrd >= Delta_Background_Tolerance,]$K <- NA)
+
+
+Plate_Analysis_L_afterQC <- ggplot(X,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC <- ggplot(X,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC <- ggplot(X,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC <- ggplot(X,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC <- ggplot(X,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC <- ggplot(X,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC <- ggplot(X,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC <- ggplot(X,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L
+Plate_Analysis_L_afterQC
+Plate_Analysis_K
+Plate_Analysis_K_afterQC
+Plate_Analysis_r
+Plate_Analysis_r_afterQC
+Plate_Analysis_AUC
+Plate_Analysis_AUC_afterQC
+Plate_Analysis_Delta_Backgrd
+Plate_Analysis_Delta_Backgrd_afterQC
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box
+Plate_Analysis_L_Box_afterQC
+Plate_Analysis_K_Box
+Plate_Analysis_K_Box_afterQC
+Plate_Analysis_r_Box
+Plate_Analysis_r_Box_afterQC
+Plate_Analysis_AUC_Box
+Plate_Analysis_AUC_Box_afterQC
+Plate_Analysis_Delta_Backgrd_Box
+Plate_Analysis_Delta_Backgrd_Box_afterQC
+dev.off()
+
+#remove the zero values and print plate analysis
+X_noZero <- X[which(X$l > 0),]
+Plate_Analysis_L_afterQC_Z <- ggplot(X_noZero,aes(Scan,l,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_afterQC_Z <- ggplot(X_noZero,aes(Scan,K,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_afterQC_Z <- ggplot(X_noZero,aes(Scan,r,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_afterQC_Z <- ggplot(X_noZero,aes(Scan,AUC,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_afterQC_Z <- ggplot(X_noZero,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2) + 
+  stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x),
+               geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+
+Plate_Analysis_L_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),l,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_Publication()
+
+Plate_Analysis_K_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),K,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for K after quality control") + theme_Publication()
+
+Plate_Analysis_r_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),r,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for r after quality control") + theme_Publication()
+
+Plate_Analysis_AUC_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),AUC,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for AUC after quality control") + theme_Publication()
+
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <- ggplot(X_noZero,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + 
+  ggtitle("Plate analysis by Drug Conc for Delta_Backgrd after quality control") + theme_Publication()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9)
+Plate_Analysis_L_afterQC_Z
+Plate_Analysis_K_afterQC_Z
+Plate_Analysis_r_afterQC_Z
+Plate_Analysis_AUC_afterQC_Z
+Plate_Analysis_Delta_Backgrd_afterQC_Z
+dev.off()
+
+#print the plate analysis data before and after QC
+pdf(file=paste(outputpath_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9)
+Plate_Analysis_L_Box_afterQC_Z
+Plate_Analysis_K_Box_afterQC_Z
+Plate_Analysis_r_Box_afterQC_Z
+Plate_Analysis_AUC_Box_afterQC_Z
+Plate_Analysis_Delta_Backgrd_Box_afterQC_Z
+dev.off()
+
+#remove dataset with zeros removed
+rm(X_noZero)
+
+
+#X_test_missing_and_removed <- X[X$Removed == 1,]
+
+#calculate summary statistics for all strains, including both background and the deletions
+X_stats_ALL <- ddply(X, c("Conc_Num","Conc_Num_Factor"), summarise,
+                     N = (length(l)),
+                     mean_L = mean(l,na.rm=TRUE),
+                     median_L = median(l,na.rm=TRUE),
+                     max_L = max(l,na.rm=TRUE),
+                     min_L = min(l,na.rm=TRUE),
+                     sd_L = sd(l,na.rm=TRUE),
+                     se_L = sd_L / sqrt(N-1),
+                     mean_K = mean(K,na.rm=TRUE),
+                     median_K = median(K,na.rm=TRUE),
+                     max_K = max(K,na.rm=TRUE),
+                     min_K = min(K,na.rm=TRUE),
+                     sd_K = sd(K,na.rm=TRUE),
+                     se_K = sd_K / sqrt(N-1),
+                     mean_r = mean(r,na.rm=TRUE),
+                     median_r = median(r,na.rm=TRUE),
+                     max_r = max(r,na.rm=TRUE),
+                     min_r = min(r,na.rm=TRUE),
+                     sd_r = sd(r,na.rm=TRUE),
+                     se_r = sd_r / sqrt(N-1),
+                     mean_AUC = mean(AUC,na.rm=TRUE),
+                     median_AUC = median(AUC,na.rm=TRUE),
+                     max_AUC = max(AUC,na.rm=TRUE),
+                     min_AUC = min(AUC,na.rm=TRUE),
+                     sd_AUC = sd(AUC,na.rm=TRUE),
+                     se_AUC = sd_AUC / sqrt(N-1)
+)
+#print(X_stats_ALL_L)
+
+write.csv(X_stats_ALL,file=paste(outputpath,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE)
+#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+##### Part 3 - Generate summary statistics and calculate the max theoretical L value
+##### Calculate the Z score at each drug conc for each deletion strain
+
+
+#get the background strains - can be modified to take another argument but for most screens will just be YDL227C
+Background_Strains <- c("YDL227C")
+
+#first part of loop will go through for each background strain 
+#most cases there will only be one YDL227C
+for(s in Background_Strains){
+  X_Background <- X[X$OrfRep == s,]
+  
+  #if there's missing data for the background strains set these values to NA so the 0 values aren't included in summary statistics
+  #we may want to consider in some cases giving the max high value to L depending on the data type
+  if(table(X_Background$l)[1] == 0){
+    X_Background[X_Background$l == 0,]$l <- NA
+    X_Background[X_Background$K == 0,]$K <- NA
+    X_Background[X_Background$r == 0,]$r <- NA
+    X_Background[X_Background$AUC == 0,]$AUC <- NA
+  }
+  
+  X_Background <- X_Background[!is.na(X_Background$l),]
+  
+  #get summary stats for L, K, R, AUC
+  X_stats_BY_L <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(l)),
+                        mean = mean(l,na.rm=TRUE),
+                        median = median(l,na.rm=TRUE),
+                        max = max(l,na.rm=TRUE),
+                        min = min(l,na.rm=TRUE),
+                        sd = sd(l,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L)
+  X1_SD <- max(X_stats_BY_L$sd)
+  
+  X_stats_BY_K <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = (length(K)),
+                        mean = mean(K,na.rm=TRUE),
+                        median = median(K,na.rm=TRUE),
+                        max = max(K,na.rm=TRUE),
+                        min = min(K,na.rm=TRUE),
+                        sd = sd(K,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_K <- max(X_stats_BY_K$sd)
+  
+  
+  X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                        N = length(r),
+                        mean = mean(r,na.rm=TRUE),
+                        median = median(r,na.rm=TRUE),
+                        max = max(r,na.rm=TRUE),
+                        min = min(r,na.rm=TRUE),
+                        sd = sd(r,na.rm=TRUE),
+                        se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_r <- max(X_stats_BY_r$sd)
+  
+  X_stats_BY_AUC <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                          N = length(AUC),
+                          mean = mean(AUC,na.rm=TRUE),
+                          median = median(AUC,na.rm=TRUE),
+                          max = max(AUC,na.rm=TRUE),
+                          min = min(AUC,na.rm=TRUE),
+                          sd = sd(AUC,na.rm=TRUE),
+                          se = sd / sqrt(N-1)
+  )
+  
+  X1_SD_AUC <- max(X_stats_BY_AUC$sd)
+  
+  X_stats_BY <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise,
+                      N = (length(l)),
+                      mean_L = mean(l,na.rm=TRUE),
+                      median_L = median(l,na.rm=TRUE),
+                      max_L = max(l,na.rm=TRUE),
+                      min_L = min(l,na.rm=TRUE),
+                      sd_L = sd(l,na.rm=TRUE),
+                      se_L = sd_L / sqrt(N-1),
+                      mean_K = mean(K,na.rm=TRUE),
+                      median_K = median(K,na.rm=TRUE),
+                      max_K = max(K,na.rm=TRUE),
+                      min_K = min(K,na.rm=TRUE),
+                      sd_K = sd(K,na.rm=TRUE),
+                      se_K = sd_K / sqrt(N-1),
+                      mean_r = mean(r,na.rm=TRUE),
+                      median_r = median(r,na.rm=TRUE),
+                      max_r = max(r,na.rm=TRUE),
+                      min_r = min(r,na.rm=TRUE),
+                      sd_r = sd(r,na.rm=TRUE),
+                      se_r = sd_r / sqrt(N-1),
+                      mean_AUC = mean(AUC,na.rm=TRUE),
+                      median_AUC = median(AUC,na.rm=TRUE),
+                      max_AUC = max(AUC,na.rm=TRUE),
+                      min_L = min(AUC,na.rm=TRUE),
+                      sd_AUC = sd(AUC,na.rm=TRUE),
+                      se_AUC = sd_AUC / sqrt(N-1)
+  )
+  
+  write.csv(X_stats_BY,file=paste(outputpath,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE)
+  
+  #calculate the max theoretical L values
+  #only look for max values when K is within 2SD of the ref strain
+  for(q in unique(X$Conc_Num_Factor)){
+    if(q == 0){
+      X_within_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[!is.na(X_within_2SD_K$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) ,]
+      X_within_2SD_K <- X_within_2SD_K[X_within_2SD_K$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+      X_outside_2SD_K <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[!is.na(X_outside_2SD_K$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1]))  ,]
+      #X_outside_2SD_K <- X_outside_2SD_K[X_outside_2SD_K$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      
+    }
+    if(q > 0){
+      X_within_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[!is.na(X_within_2SD_K_temp$l),]
+      X_stats_TEMP_K <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K_temp <- X_within_2SD_K_temp[X_within_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])),]
+      X_within_2SD_K <- rbind(X_within_2SD_K,X_within_2SD_K_temp)
+      
+      X_outside_2SD_K_temp <- X[X$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[!is.na(X_outside_2SD_K_temp$l),]
+      #X_outside_2SD_K_Temp <- X_stats_BY_K[X_stats_BY_K$Conc_Num_Factor == q,]
+      X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K <= (X_stats_TEMP_K$mean[1] - (2*X_stats_TEMP_K$sd[1])) | X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      #X_outside_2SD_K_temp <- X_outside_2SD_K_temp[X_outside_2SD_K_temp$K >= (X_stats_TEMP_K$mean[1] + (2*X_stats_TEMP_K$sd[1])) ,]
+      X_outside_2SD_K <- rbind(X_outside_2SD_K,X_outside_2SD_K_temp)
+    }
+  }
+  
+  X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                     N = (length(l)),
+                                     mean = mean(l),
+                                     median = median(l),
+                                     max = max(l,na.rm=TRUE),
+                                     min = min(l,na.rm=TRUE),
+                                     sd = sd(l),
+                                     se = sd / sqrt(N-1),
+                                     z_max = (max-mean)/sd
+  )
+  print(X_stats_BY_L_within_2SD_K)
+  X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd)
+  write.csv(X_stats_BY_L_within_2SD_K,file=paste(outputpath_QC,"Max_Observed_L_Vals_for_spots_within_2SD_K.csv",sep=""),row.names=FALSE)
+  
+  X_stats_BY_L_outside_2SD_K <- ddply(X_outside_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise,
+                                      N = (length(l)),
+                                      mean = mean(l),
+                                      median = median(l),
+                                      max = max(l,na.rm=TRUE),
+                                      min = min(l,na.rm=TRUE),
+                                      sd = sd(l),
+                                      se = sd / sqrt(N-1)
+  )
+  print(X_stats_BY_L_outside_2SD_K)
+  X1_SD_outside_2SD_K <- max(X_stats_BY_L_outside_2SD_K$sd)
+  
+  #X1_SD_outside_2SD_K <- X[X$l %in% X1_SD_within_2SD_K$l,]
+  Outside_2SD_K_L_vs_K <- ggplot(X_outside_2SD_K,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) +
+    ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  print(Outside_2SD_K_L_vs_K)
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_L_vs_K)
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"RawL_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  Outside_2SD_K_delta_background_vs_K <- ggplot(X_outside_2SD_K,aes(Delta_Backgrd,K,color=as.factor(Conc_Num))) + geom_point(aes(l=l,ORF=ORF,Gene=Gene),shape=3,position="jitter") +
+    ggtitle("DeltaBackground vs K for strains falling outside 2SD of the K mean at each conc") + theme_Publication_legend_right()
+  pdf(paste(outputpath_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8)
+  Outside_2SD_K_delta_background_vs_K
+  dev.off()
+  pgg <- ggplotly(Outside_2SD_K_delta_background_vs_K)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath_QC,"DeltaBackground_vs_K_for_strains_outside_2SD_K.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  
+  #get the background strain mean values at the no drug conc to calculate shift
+  Background_L <- X_stats_BY_L$mean[1]
+  Background_K <- X_stats_BY_K$mean[1]
+  Background_r <- X_stats_BY_r$mean[1]
+  Background_AUC <- X_stats_BY_AUC$mean[1]
+  
+  #create empty plots for plotting element
+  p_l <-  ggplot()
+  p_K <-  ggplot()
+  p_r <-  ggplot()
+  p_AUC <-  ggplot()
+  
+  p_rf_l <-  ggplot()
+  p_rf_K <-  ggplot()
+  p_rf_r <-  ggplot()
+  p_rf_AUC <-  ggplot()
+  
+  #get only the deletion strains
+  X2 <- X
+  X2 <- X2[X2$OrfRep != "YDL227C",]
+  
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2$Conc_Num))){
+    Concentration <- unique(X2$Conc_Num)[i]
+    X2_temp <- X2[X2$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_new <- X2_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      
+      #X2_temp[X2_temp$K == 0,]$K <- X_stats_ALL_K$max[i]
+      #X2_temp[X2_temp$r == 0,]$r <- X_stats_ALL_r$max[i]
+      #X2_temp[X2_temp$AUC == 0,]$AUC <- X_stats_ALL_AUC$max[i]
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+      
+      X2_new <- rbind(X2_new,X2_temp)
+      
+    }
+  }
+  X2 <- X2_new
+  
+  
+  #get only the RF strains
+  X2_RF <- X
+  X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",]
+  #if set to max theoretical value, add a 1 to SM, if not, leave as 0
+  #SM = Set to Max
+  X2_RF$SM <- 0
+  #set the missing values to the highest theoretical value at each drug conc for L, leave other values as 0 for the max/min
+  for(i in 1:length(unique(X2_RF$Conc_Num))){
+    Concentration <- unique(X2_RF$Conc_Num)[i]
+    X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,]
+    if(Concentration == 0){
+      X2_RF_new <- X2_RF_temp
+      print(paste("Check loop order, conc =",Concentration,sep=" "))
+    }
+    if(Concentration > 0){
+      try(X2_RF_temp[X2_RF_temp$l == 0 & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      try(X2_temp[X2_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_temp$l),]$SM <- 1)
+      try(X2_RF_temp[X2_RF_temp$l >= X_stats_BY_L_within_2SD_K$max[i] & !is.na(X2_RF_temp$l),]$l <- X_stats_BY_L_within_2SD_K$max[i])
+      print(paste("Check loop order, if error, refs have no L values outside theoretical max L, for REFs, conc =",Concentration,sep=" "))
+      
+      X2_RF_new <- rbind(X2_RF_new,X2_RF_temp)
+      
+    }
+  }
+  X2_RF <- X2_RF_new
+  
+  
+  #######Part 4 Get the RF Z score values
+  #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score
+  X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_")
+  
+  num_genes_RF <- length(unique(X2_RF$OrfRep))
+  print(num_genes_RF)
+  
+  
+  #create the output data.frame containing columns for each RF strain
+  InteractionScores_RF <- unique(X2_RF["OrfRep"])
+  #InteractionScores_RF$Gene <- unique(X2$Gene)
+  InteractionScores_RF$Gene <- NA
+  InteractionScores_RF$Raw_Shift_L <- NA
+  InteractionScores_RF$Z_Shift_L <- NA
+  InteractionScores_RF$lm_Score_L <- NA
+  InteractionScores_RF$Z_lm_L <- NA  
+  InteractionScores_RF$R_Squared_L <- NA
+  InteractionScores_RF$Sum_Z_Score_L <- NA
+  InteractionScores_RF$Avg_Zscore_L <- NA
+  InteractionScores_RF$Raw_Shift_K <- NA
+  InteractionScores_RF$Z_Shift_K <- NA
+  InteractionScores_RF$lm_Score_K <- NA
+  InteractionScores_RF$Z_lm_K <- NA  
+  InteractionScores_RF$R_Squared_K <- NA
+  InteractionScores_RF$Sum_Z_Score_K <- NA
+  InteractionScores_RF$Avg_Zscore_K <- NA
+  InteractionScores_RF$Raw_Shift_r <- NA
+  InteractionScores_RF$Z_Shift_r <- NA
+  InteractionScores_RF$lm_Score_r <- NA
+  InteractionScores_RF$Z_lm_r <- NA
+  InteractionScores_RF$R_Squared_r <- NA
+  InteractionScores_RF$Sum_Z_Score_r <- NA
+  InteractionScores_RF$Avg_Zscore_r <- NA
+  InteractionScores_RF$Raw_Shift_AUC <- NA
+  InteractionScores_RF$Z_Shift_AUC <- NA
+  InteractionScores_RF$lm_Score_AUC <- NA
+  InteractionScores_RF$Z_lm_AUC <- NA
+  InteractionScores_RF$R_Squared_AUC <- NA
+  InteractionScores_RF$Sum_Z_Score_AUC <- NA
+  InteractionScores_RF$Avg_Zscore_AUC <- NA
+  InteractionScores_RF$NG <- NA
+  InteractionScores_RF$SM <- NA
+  
+  
+  for(i in 1:num_genes_RF){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2_RF$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2_RF[X2_RF$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      gene_lm_AUC <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      gene_interaction_AUC <- NA
+      r_squared_AUC <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores_RF$OrfRep[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$OrfRep[1]
+      InteractionScores_RF$Gene[InteractionScores_RF$OrfRep == Gene_Sel] <- X_Gene_Sel$Gene[1]
+      
+      InteractionScores_RF$Raw_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores_RF$Z_Shift_L[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores_RF$lm_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores_RF$R_Squared_L[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores_RF$Sum_Z_Score_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_L[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores_RF$Z_Shift_K[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores_RF$lm_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores_RF$R_Squared_K[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores_RF$Sum_Z_Score_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_K[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores_RF$Z_Shift_r[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores_RF$lm_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores_RF$R_Squared_r[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores_RF$Sum_Z_Score_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_r[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores_RF$Raw_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores_RF$Z_Shift_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores_RF$lm_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores_RF$R_Squared_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores_RF$Sum_Z_Score_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+      InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction)
+    }
+    
+    InteractionScores_RF$NG[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores_RF$DB[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores_RF$SM[InteractionScores_RF$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass RF Calculation loop")
+  
+  lm_sd_L <- sd(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_sd_K <- sd(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_sd_r <- sd(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_sd_AUC <- sd(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  lm_mean_L <- mean(InteractionScores_RF$lm_Score_L,na.rm=TRUE)
+  lm_mean_K <- mean(InteractionScores_RF$lm_Score_K,na.rm=TRUE)
+  lm_mean_r <- mean(InteractionScores_RF$lm_Score_r,na.rm=TRUE)
+  lm_mean_AUC <- mean(InteractionScores_RF$lm_Score_AUC,na.rm=TRUE)
+  
+  print(paste("Mean RF linear regression score L",lm_mean_L))
+  
+  
+  InteractionScores_RF$Z_lm_L <- (InteractionScores_RF$lm_Score_L - lm_mean_L)/(lm_sd_L)
+  InteractionScores_RF$Z_lm_K <- (InteractionScores_RF$lm_Score_K - lm_mean_K)/(lm_sd_K)
+  InteractionScores_RF$Z_lm_r <- (InteractionScores_RF$lm_Score_r - lm_mean_r)/(lm_sd_r)
+  InteractionScores_RF$Z_lm_AUC <- (InteractionScores_RF$lm_Score_AUC - lm_mean_AUC)/(lm_sd_AUC)
+  
+  
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$Z_lm_L,decreasing=TRUE),]
+  InteractionScores_RF <- InteractionScores_RF[order(InteractionScores_RF$NG,decreasing=TRUE),]
+  write.csv(InteractionScores_RF,paste(outputpath,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  
+  for(i in 1:num_genes_RF){
+    Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL_RF[X_stats_interaction_ALL_RF$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores_RF[InteractionScores_RF$OrfRep == Gene_Sel,]
+    
+    p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg Zscore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm Zscore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_rf_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_rf_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("lm ZScore =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_RF_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_RF_final <- rbind(X_stats_interaction_ALL_RF_final,X_ZCalculations)
+    }
+  }
+  print("Pass RF ggplot loop")
+  write.csv(X_stats_interaction_ALL_RF_final,paste(outputpath,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  ####### Part 5 - Get Zscores for Gene deletion strains
+  
+  
+  
+  #get total number of genes for the next loop
+  num_genes <- length(unique(X2$OrfRep))
+  print(num_genes)
+  
+  #create the output data.frame containing columns for each deletion strain
+  InteractionScores <- unique(X2["OrfRep"])
+  #InteractionScores$Gene <- unique(X2$Gene)
+  InteractionScores$Gene <- NA
+  InteractionScores$Raw_Shift_L <- NA
+  InteractionScores$Z_Shift_L <- NA
+  InteractionScores$lm_Score_L <- NA
+  InteractionScores$Z_lm_L <- NA
+  InteractionScores$R_Squared_L <- NA
+  InteractionScores$Sum_Z_Score_L <- NA
+  InteractionScores$Avg_Zscore_L <- NA
+  InteractionScores$Raw_Shift_K <- NA
+  InteractionScores$Z_Shift_K <- NA
+  InteractionScores$lm_Score_K <- NA
+  InteractionScores$Z_lm_K <- NA
+  InteractionScores$R_Squared_K <- NA
+  InteractionScores$Sum_Z_Score_K <- NA
+  InteractionScores$Avg_Zscore_K <- NA
+  InteractionScores$Raw_Shift_r <- NA
+  InteractionScores$Z_Shift_r <- NA
+  InteractionScores$lm_Score_r <- NA
+  InteractionScores$Z_lm_r <- NA
+  InteractionScores$R_Squared_r <- NA
+  InteractionScores$Sum_Z_Score_r <- NA
+  InteractionScores$Avg_Zscore_r <- NA
+  InteractionScores$Raw_Shift_AUC <- NA
+  InteractionScores$Z_Shift_AUC <- NA
+  InteractionScores$lm_Score_AUC <- NA
+  InteractionScores$Z_lm_AUC <- NA
+  InteractionScores$R_Squared_AUC <- NA
+  InteractionScores$Sum_Z_Score_AUC <- NA
+  InteractionScores$Avg_Zscore_AUC <- NA
+  InteractionScores$NG <- NA
+  InteractionScores$DB <- NA
+  InteractionScores$SM <- NA
+  
+  for(i in 1:num_genes){
+    #get each deletion strain ORF
+    Gene_Sel <- unique(X2$OrfRep)[i]
+    #extract only the current deletion strain and its data
+    X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel,] 
+    
+    X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise,
+                                 N = (length(l)),
+                                 mean_L = mean(l,na.rm = TRUE),
+                                 median_L = median(l,na.rm = TRUE),
+                                 sd_L = sd(l,na.rm = TRUE),
+                                 se_L = sd_L / sqrt(N-1),
+                                 mean_K = mean(K,na.rm = TRUE),
+                                 median_K = median(K,na.rm = TRUE),
+                                 sd_K = sd(K,na.rm = TRUE),
+                                 se_K = sd_K / sqrt(N-1),
+                                 mean_r = mean(r,na.rm = TRUE),
+                                 median_r = median(r,na.rm = TRUE),
+                                 sd_r = sd(r,na.rm = TRUE),
+                                 se_r = sd_r / sqrt(N-1),
+                                 mean_AUC = mean(AUC,na.rm = TRUE),
+                                 median_AUC = median(AUC,na.rm = TRUE),
+                                 sd_AUC = sd(AUC,na.rm = TRUE),
+                                 se_AUC = sd_AUC / sqrt(N-1),
+                                 NG = sum(NG,na.rm=TRUE),
+                                 DB= sum(DB,na.rm=TRUE),
+                                 SM = sum(SM,na.rm=TRUE)
+    )
+    
+    
+    #Get shift vals
+    #if L is 0, that means the no growth on no drug
+    #if L is NA at 0, that means the spot was removed due to contamination
+    #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift
+    #otherwise calculate shift at no drug conc
+    if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){
+      X_stats_interaction$Raw_Shift_L <- 0
+      X_stats_interaction$Raw_Shift_K <- 0
+      X_stats_interaction$Raw_Shift_r <- 0
+      X_stats_interaction$Raw_Shift_AUC <- 0
+      X_stats_interaction$Z_Shift_L <- 0
+      X_stats_interaction$Z_Shift_K <- 0
+      X_stats_interaction$Z_Shift_r <- 0
+      X_stats_interaction$Z_Shift_AUC <- 0
+    }else{
+      X_stats_interaction$Raw_Shift_L <- X_stats_interaction$mean_L[1] - Background_L
+      X_stats_interaction$Raw_Shift_K <- X_stats_interaction$mean_K[1] - Background_K
+      X_stats_interaction$Raw_Shift_r <- X_stats_interaction$mean_r[1] - Background_r
+      X_stats_interaction$Raw_Shift_AUC <- X_stats_interaction$mean_AUC[1] - Background_AUC
+      X_stats_interaction$Z_Shift_L <- X_stats_interaction$Raw_Shift_L[1]/X_stats_BY_L$sd[1]
+      X_stats_interaction$Z_Shift_K <- X_stats_interaction$Raw_Shift_K[1]/X_stats_BY_K$sd[1]
+      X_stats_interaction$Z_Shift_r <- X_stats_interaction$Raw_Shift_r[1]/X_stats_BY_r$sd[1]
+      X_stats_interaction$Z_Shift_AUC <- X_stats_interaction$Raw_Shift_AUC[1]/X_stats_BY_AUC$sd[1]
+    }
+    
+    
+    #get WT vals
+    X_stats_interaction$WT_l <- X_stats_BY_L$mean
+    X_stats_interaction$WT_K <- X_stats_BY_K$mean
+    X_stats_interaction$WT_r <- X_stats_BY_r$mean
+    X_stats_interaction$WT_AUC <- X_stats_BY_AUC$mean
+    
+    #Get WT SD
+    X_stats_interaction$WT_sd_l <- X_stats_BY_L$sd
+    X_stats_interaction$WT_sd_K <- X_stats_BY_K$sd
+    X_stats_interaction$WT_sd_r <- X_stats_BY_r$sd
+    X_stats_interaction$WT_sd_AUC <- X_stats_BY_AUC$sd
+    
+    
+    #only get scores if there's growth at no drug
+    if(X_stats_interaction$mean_L[1] != 0 & !is.na(X_stats_interaction$mean_L[1])){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for no growth values in Z score calculation
+      if(sum(X_stats_interaction$NG,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+        
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #get linear model
+      gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor,data = X_stats_interaction)
+      gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor,data = X_stats_interaction)
+      
+      #get interaction score calculated by linear model and R-squared value for the fit
+      gene_interaction_L <- MAX_CONC*(gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1]
+      r_squared_l <- summary(gene_lm_L)$r.squared
+      gene_interaction_K <- MAX_CONC*(gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1]
+      r_squared_K <- summary(gene_lm_K)$r.squared
+      gene_interaction_r <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1]
+      r_squared_r <- summary(gene_lm_r)$r.squared
+      gene_interaction_AUC <- MAX_CONC*(gene_lm_r$coefficients[2]) + gene_lm_AUC$coefficients[1]
+      r_squared_AUC <- summary(gene_lm_AUC)$r.squared
+      
+      #Get total of non removed values
+      Num_non_Removed_Conc <- Total_Conc_Nums - sum(X_stats_interaction$DB,na.rm = TRUE) - 1
+      
+      #report the scores
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_L
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_L-lm_mean_L)/lm_sd_L
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_L,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_K
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_K-lm_mean_K)/lm_sd_K
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_K,na.rm = TRUE)/(Num_non_Removed_Conc)
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_r
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_r-lm_mean_r)/lm_sd_r
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_r,na.rm = TRUE)/(Total_Conc_Nums-1) 
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- gene_interaction_AUC
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- (gene_interaction_AUC-lm_mean_AUC)/lm_sd_AUC
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) 
+    }
+    
+    if(X_stats_interaction$mean_L[1] == 0 | is.na(X_stats_interaction$mean_L[1]) ){
+      
+      #calculate expected values
+      X_stats_interaction$Exp_L <- X_stats_interaction$WT_l + X_stats_interaction$Raw_Shift_L
+      X_stats_interaction$Exp_K <- X_stats_interaction$WT_K + X_stats_interaction$Raw_Shift_K
+      X_stats_interaction$Exp_r <- X_stats_interaction$WT_r + X_stats_interaction$Raw_Shift_r
+      X_stats_interaction$Exp_AUC <- X_stats_interaction$WT_AUC + X_stats_interaction$Raw_Shift_AUC
+      
+      #calculate normalized delta values
+      X_stats_interaction$Delta_L <- X_stats_interaction$mean_L - X_stats_interaction$Exp_L 
+      X_stats_interaction$Delta_K <- X_stats_interaction$mean_K - X_stats_interaction$Exp_K
+      X_stats_interaction$Delta_r <-X_stats_interaction$mean_r - X_stats_interaction$Exp_r
+      X_stats_interaction$Delta_AUC <- X_stats_interaction$mean_AUC - X_stats_interaction$Exp_AUC
+      
+      #disregard shift for missing values in Z score calculatiom
+      if(sum(X_stats_interaction$NG,na.rm = TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_L - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_l 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_K - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_K 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_r - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_r 
+        X_stats_interaction[X_stats_interaction$NG == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$NG == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$NG == 1,]$WT_AUC 
+      }
+      #disregard shift for set to max values in Z score calculation
+      if(sum(X_stats_interaction$SM,na.rm=TRUE) > 0){
+        X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_L <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_L - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_l 
+        #only calculate the L value without shift since L is the only adjusted value
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_K <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_K - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_K 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_r <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_r - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_r 
+        #X_stats_interaction[X_stats_interaction$SM == 1,]$Delta_AUC <- X_stats_interaction[X_stats_interaction$SM == 1,]$mean_AUC - X_stats_interaction[X_stats_interaction$SM == 1,]$WT_AUC 
+        
+      }
+      
+      
+      #calculate Z score at each concentration
+      X_stats_interaction$Zscore_L <- (X_stats_interaction$Delta_L)/(X_stats_interaction$WT_sd_l)
+      X_stats_interaction$Zscore_K <- (X_stats_interaction$Delta_K)/(X_stats_interaction$WT_sd_K)
+      X_stats_interaction$Zscore_r <- (X_stats_interaction$Delta_r)/(X_stats_interaction$WT_sd_r)
+      X_stats_interaction$Zscore_AUC <- (X_stats_interaction$Delta_AUC)/(X_stats_interaction$WT_sd_AUC)
+      
+      #NA values for the next part since there's an NA or 0 at the no drug.
+      gene_lm_L <- NA
+      gene_lm_K <- NA
+      gene_lm_r <- NA
+      
+      gene_interaction_L <- NA
+      r_squared_l <- NA
+      gene_interaction_K <- NA
+      r_squared_K <- NA
+      gene_interaction_r <- NA
+      r_squared_r <- NA
+      
+      X_stats_interaction$Raw_Shift_L <- NA
+      X_stats_interaction$Raw_Shift_K <- NA
+      X_stats_interaction$Raw_Shift_r <- NA
+      X_stats_interaction$Raw_Shift_AUC <- NA
+      
+      X_stats_interaction$Z_Shift_L <- NA
+      X_stats_interaction$Z_Shift_K <- NA
+      X_stats_interaction$Z_Shift_r <- NA
+      X_stats_interaction$Z_Shift_AUC <- NA
+      
+      InteractionScores$OrfRep[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$OrfRep[1])
+      InteractionScores$Gene[InteractionScores$OrfRep == Gene_Sel] <- as.character(X_Gene_Sel$Gene[1])
+      
+      InteractionScores$Raw_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_L[1]
+      InteractionScores$Z_Shift_L[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_L[1]
+      InteractionScores$lm_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_L[InteractionScores$OrfRep == Gene_Sel] <- r_squared_l
+      InteractionScores$Sum_Z_Score_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_L[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_K[1]
+      InteractionScores$Z_Shift_K[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_K[1]
+      InteractionScores$lm_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_K[InteractionScores$OrfRep == Gene_Sel] <-NA
+      InteractionScores$R_Squared_K[InteractionScores$OrfRep == Gene_Sel] <- r_squared_K
+      InteractionScores$Sum_Z_Score_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_K[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_r[1]
+      InteractionScores$Z_Shift_r[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_r[1]
+      InteractionScores$lm_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_r[InteractionScores$OrfRep == Gene_Sel] <- r_squared_r
+      InteractionScores$Sum_Z_Score_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_r[InteractionScores$OrfRep == Gene_Sel] <- NA
+      
+      InteractionScores$Raw_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Raw_Shift_AUC[1]
+      InteractionScores$Z_Shift_AUC[InteractionScores$OrfRep == Gene_Sel] <- X_stats_interaction$Z_Shift_AUC[1]
+      InteractionScores$lm_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Z_lm_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$R_Squared_AUC[InteractionScores$OrfRep == Gene_Sel] <- r_squared_AUC
+      InteractionScores$Sum_Z_Score_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+      InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA
+    }
+    
+    if(i == 1){
+      X_stats_interaction_ALL <- X_stats_interaction
+    }
+    if(i > 1){
+      X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction)
+    }
+    
+    InteractionScores$NG[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$NG,na.rm = TRUE)
+    InteractionScores$DB[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$DB,na.rm = TRUE)
+    InteractionScores$SM[InteractionScores$OrfRep == Gene_Sel] <- sum(X_stats_interaction$SM,na.rm = TRUE)
+    
+    #X_stats_L_int_temp <- rbind(X_stats_L_int_temp,X_stats_L_int)
+    
+    
+  }
+  print("Pass Int Calculation loop")
+  InteractionScores <- InteractionScores[order(InteractionScores$Z_lm_L,decreasing=TRUE),]
+  InteractionScores <- InteractionScores[order(InteractionScores$NG,decreasing=TRUE),]
+  df_order_by_OrfRep <- unique(InteractionScores$OrfRep)
+  #X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG,decreasing=TRUE),]
+  write.csv(InteractionScores,paste(outputpath,"ZScores_Interaction.csv",sep=""),row.names=FALSE)
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_K <- InteractionScores[InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores[InteractionScores$Avg_Zscore_L >= 2 | InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores[InteractionScores$Avg_Zscore_K >= 2 | InteractionScores$Avg_Zscore_K <= -2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores[InteractionScores$Z_lm_L >= 2 & InteractionScores$Avg_Zscore_L <= -2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores[InteractionScores$Z_lm_L <= -2 & InteractionScores$Avg_Zscore_L >= 2,]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores[InteractionScores$Z_lm_K <= -2 & InteractionScores$Avg_Zscore_K >= 2,]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores[InteractionScores$Z_lm_K >= 2 & InteractionScores$Avg_Zscore_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L <- InteractionScores_deletion_enhancers_L[!is.na(InteractionScores_deletion_enhancers_L$OrfRep),]
+  InteractionScores_deletion_enhancers_K <- InteractionScores_deletion_enhancers_K[!is.na(InteractionScores_deletion_enhancers_K$OrfRep),]
+  InteractionScores_deletion_suppressors_L <- InteractionScores_deletion_suppressors_L[!is.na(InteractionScores_deletion_suppressors_L$OrfRep),]
+  InteractionScores_deletion_suppressors_K <- InteractionScores_deletion_suppressors_K[!is.na(InteractionScores_deletion_suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L <- InteractionScores_deletion_enhancers_and_Suppressors_L[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K <- InteractionScores_deletion_enhancers_and_Suppressors_K[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L$OrfRep),]
+  InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K <- InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K[!is.na(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K$OrfRep),]
+  InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K <- InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[!is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outputpath,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outputpath,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE)
+  
+  #get enhancers and suppressors for linear regression
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2,]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores[InteractionScores$Z_lm_K <= -2,]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores[InteractionScores$Z_lm_L >= 2 | InteractionScores$Z_lm_L <= -2,]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores[InteractionScores$Z_lm_K >= 2 | InteractionScores$Z_lm_K <= -2,]
+  
+  InteractionScores_deletion_enhancers_L_lm <- InteractionScores_deletion_enhancers_L_lm[!is.na(InteractionScores_deletion_enhancers_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_K_lm <- InteractionScores_deletion_enhancers_K_lm[!is.na(InteractionScores_deletion_enhancers_K_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_L_lm <- InteractionScores_deletion_suppressors_L_lm[!is.na(InteractionScores_deletion_suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_suppressors_K_lm <- InteractionScores_deletion_suppressors_K_lm[!is.na(InteractionScores_deletion_suppressors_K_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_L_lm <- InteractionScores_deletion_enhancers_and_Suppressors_L_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_L_lm$OrfRep),]
+  InteractionScores_deletion_enhancers_and_Suppressors_K_lm <- InteractionScores_deletion_enhancers_and_Suppressors_K_lm[!is.na(InteractionScores_deletion_enhancers_and_Suppressors_K_lm$OrfRep),]
+  
+  write.csv(InteractionScores_deletion_enhancers_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE)
+  write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outputpath,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE)
+  
+  
+  write.csv(Labels,file=paste("../Code/StudyInfo.csv"),row.names = FALSE)
+  print('ln 1570 write StudyInfo.csv after ')
+        #write.table(Labels,file=paste("../Code/StudyInfo.txt"),sep="\t",row.names = FALSE)
+  
+  for(i in 1:num_genes){
+    Gene_Sel <- unique(InteractionScores$OrfRep)[i]
+    X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$OrfRep == Gene_Sel,]
+    X_Int_Scores <- InteractionScores[InteractionScores$OrfRep == Gene_Sel,]
+    
+    p_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_L,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_L,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_L,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-65,65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_K,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_K,2))) + 
+      annotate("text",x=1,y=25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_K,2))) +
+      annotate("text",x=1,y=-25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-60,-50,-40,-30,-20,-10,0,10,20,30,40,50,60)) + 
+      theme_Publication()
+    
+    p_r[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_r)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-0.65,0.65)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_r), ymax=0+(2*WT_sd_r)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=0.45,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_r,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=0.35,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_r,2))) + 
+      annotate("text",x=1,y=0.25,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_r,2))) +
+      annotate("text",x=1,y=-0.25,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-0.35,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-0.45,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-0.6,-0.4,-0.2,0,0.2,0.4,0.6)) + 
+      theme_Publication()
+    
+    p_AUC[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_AUC)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) +
+      coord_cartesian(ylim = c(-6500,6500)) +
+      geom_errorbar(aes(ymin=0-(2*WT_sd_AUC), ymax=0+(2*WT_sd_AUC)),alpha=0.3) + 
+      ggtitle(paste(X_ZCalculations$OrfRep[1],X_ZCalculations$Gene[1],sep="      ")) +
+      annotate("text",x=1,y=4500,label = paste("ZShift =",round(X_Int_Scores$Z_Shift_AUC,2))) + scale_color_discrete(guide = FALSE) + 
+      #annotate("text",x=1,y=3500,label = paste("Avg ZScore =",round(X_Int_Scores$Avg_Zscore_AUC,2))) + 
+      annotate("text",x=1,y=2500,label = paste("Z lm Score =",round(X_Int_Scores$Z_lm_AUC,2))) +
+      annotate("text",x=1,y=-2500,label = paste("NG =",X_Int_Scores$NG)) +
+      annotate("text",x=1,y=-3500,label = paste("DB =",X_Int_Scores$DB)) +
+      annotate("text",x=1,y=-4500,label = paste("SM =",X_Int_Scores$SM)) +
+      scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X_ZCalculations$Conc_Num_Factor),labels = unique(as.character(X_ZCalculations$Conc_Num))) +
+      scale_y_continuous(breaks = c(-6000,-5000,-4000,-3000,-2000,-1000,0,1000,2000,3000,4000,5000,6000)) + 
+      theme_Publication()
+    
+    
+    
+    if(i == 1){
+      X_stats_interaction_ALL_final <- X_ZCalculations
+    }
+    if(i > 1){
+      X_stats_interaction_ALL_final <- rbind(X_stats_interaction_ALL_final,X_ZCalculations)
+    }
+  }
+  print("Pass Int ggplot loop")
+  write.csv(X_stats_interaction_ALL_final,paste(outputpath,"ZScore_Calculations.csv",sep=""),row.names = FALSE)
+  
+  
+  
+  
+  
+  
+  Blank <- ggplot(X2_RF) + geom_blank()
+  
+  pdf(paste(outputpath,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_l[[num]],p_K[[num]],p_r[[num]],p_AUC[[num]],p_l[[num+1]],p_K[[num+1]],p_r[[num+1]],p_AUC[[num+1]],p_l[[num+2]],p_K[[num+2]],p_r[[num+2]],p_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_l[[364]],p_K[[364]],p_r[[364]],p_l[[365]],p_K[[365]],p_r[[365]],p_l[[366]],p_K[[366]],p_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_K[[num+3]],p_r[[num+3]],p1[[num+4]],p_K[[num+4]],p_r[[num+4]]
+  }
+  if(num_genes != (num+2)){
+    total_num = num_genes - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],p_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],p_l[[num+5]],p_K[[num+5]],p_r[[num+5]],p_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],p_l[[num+4]],p_K[[num+4]],p_r[[num+4]],p_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_l[[num+3]],p_K[[num+3]],p_r[[num+3]],p_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_l[[num+6]],p_K[[num+6]],p_r[[num+6]],p_l[[num+7]],p_K[[num+7]],p_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  
+  
+  pdf(paste(outputpath,"RF_InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE)
+  
+  X_stats_X2_RF <- ddply(X2_RF, c("Conc_Num","Conc_Num_Factor"), summarise,
+                         mean_L = mean(l,na.rm=TRUE),
+                         median_L = median(l,na.rm=TRUE),
+                         max_L = max(l,na.rm=TRUE),
+                         min_L = min(l,na.rm=TRUE),
+                         sd_L = sd(l,na.rm=TRUE),
+                         mean_K = mean(K,na.rm=TRUE),
+                         median_K = median(K,na.rm=TRUE),
+                         max_K = max(K,na.rm=TRUE),
+                         min_K = min(K,na.rm=TRUE),
+                         sd_K = sd(K,na.rm=TRUE),
+                         mean_r = mean(r,na.rm=TRUE),
+                         median_r = median(r,na.rm=TRUE),
+                         max_r = max(r,na.rm=TRUE),
+                         min_r = min(r,na.rm=TRUE),
+                         sd_r = sd(r,na.rm=TRUE),
+                         mean_AUC = mean(AUC,na.rm=TRUE),
+                         median_AUC = median(AUC,na.rm=TRUE),
+                         max_AUC = max(AUC,na.rm=TRUE),
+                         min_AUC = min(AUC,na.rm=TRUE),
+                         sd_AUC = sd(AUC,na.rm=TRUE),
+                         NG = sum(NG,na.rm=TRUE),
+                         DB = sum(DB,na.rm=TRUE),
+                         SM = sum(SM,na.rm=TRUE)
+  )
+  
+  
+  L_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,l)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    annotate("text",x=-0.25,y=10,label="NG") +
+    annotate("text",x=-0.25,y=5,label="DB") +
+    annotate("text",x=-0.25,y=0,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=0,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  K_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,K)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(-20,160)) + 
+    annotate("text",x=-0.25,y=-5,label="NG") +
+    annotate("text",x=-0.25,y=-12.5,label="DB") +
+    annotate("text",x=-0.25,y=-20,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-5,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-12.5,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=-20,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  R_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,r)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    annotate("text",x=-0.25,y=.9,label="NG") +
+    annotate("text",x=-0.25,y=.8,label="DB") +
+    annotate("text",x=-0.25,y=.7,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.9,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.8,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=.7,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) +  
+    stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), 
+                 geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") +
+    scale_x_continuous(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(0,12500)) + 
+    annotate("text",x=-0.25,y=11000,label="NG") +
+    annotate("text",x=-0.25,y=10000,label="DB") +
+    annotate("text",x=-0.25,y=9000,label="SM") +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=11000,label=X_stats_X2_RF$NG) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=10000,label=X_stats_X2_RF$DB) +
+    annotate("text",x=c(unique(X2_RF$Conc_Num_Factor)),y=9000,label=X_stats_X2_RF$SM) +
+    theme_Publication()
+  
+  
+  L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + 
+    theme_Publication()
+  
+  K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + 
+    theme_Publication()  
+  
+  r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for r with SD", sep = " ")) + coord_cartesian(ylim=c(0,1)) + 
+    theme_Publication()
+  
+  AUC_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),AUC)) + geom_boxplot() +  
+    scale_x_discrete(name = unique(X$Drug[1]),breaks = unique(X2_RF$Conc_Num_Factor),labels = as.character(unique(X2_RF$Conc_Num))) +
+    ggtitle(paste(s,"Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim=c(12000,0)) + 
+    theme_Publication()
+  
+  
+  grid.arrange(L_Stats,K_Stats,R_Stats,AUC_Stats,ncol=2,nrow=2)
+  grid.arrange(L_Stats_Box,K_Stats_Box,r_Stats_Box,AUC_Stats_Box,ncol=2,nrow=2)
+  
+  
+  
+  #plot the references
+  #grid.arrange(p3,p3_K,p3_r,p4,p4_K,p4_r,p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=4)
+  #grid.arrange(p5,p5_K,p5_r,p6,p6_K,p6_r,ncol=3,nrow=2)
+  
+  #loop for grid arrange 4x3 
+  j <- rep(1,((num_genes_RF)/3)-1)
+  for(n in 1:length(j)){
+    j[n+1] <- n*3 + 1
+  }
+  #loop for printing each plot
+  num <- 0
+  for(m in 1:(round((num_genes_RF)/3)-1)){
+    num <- j[m]
+    grid.arrange(p_rf_l[[num]],p_rf_K[[num]],p_rf_r[[num]],p_rf_AUC[[num]],p_rf_l[[num+1]],p_rf_K[[num+1]],p_rf_r[[num+1]],p_rf_AUC[[num+1]],p_rf_l[[num+2]],p_rf_K[[num+2]],p_rf_r[[num+2]],p_rf_AUC[[num+2]],ncol=4,nrow=3)
+    #grid.arrange(p_rf_l[[364]],p_rf_K[[364]],p_rf_r[[364]],p_rf_l[[365]],p_rf_K[[365]],p_rf_r[[365]],p_rf_l[[366]],p_rf_K[[366]],p_rf_r[[366]],ncol=3,nrow=3)
+    
+    
+    #p1[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p1[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]]
+  }
+  if(num_genes_RF != (num+2)){
+    total_num = num_genes_RF - (num+2)
+    if(total_num == 5){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],p_rf_AUC[[num+7]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    if(total_num == 4){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_AUC[[num+6]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+    }
+    
+    if(total_num == 3){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],p_rf_l[[num+5]],p_rf_K[[num+5]],p_rf_r[[num+5]],p_rf_AUC[[num+5]],ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 2){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],p_rf_l[[num+4]],p_rf_K[[num+4]],p_rf_r[[num+4]],p_rf_AUC[[num+4]],Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+    
+    if(total_num == 1){
+      grid.arrange(p_rf_l[[num+3]],p_rf_K[[num+3]],p_rf_r[[num+3]],p_rf_AUC[[num+3]],Blank,Blank,Blank,Blank,Blank,Blank,Blank,Blank,ncol=4,nrow=3)
+      #grid.arrange(p_rf_l[[num+6]],p_rf_K[[num+6]],p_rf_r[[num+6]],p_rf_l[[num+7]],p_rf_K[[num+7]],p_rf_r[[num+7]],Blank,Blank,Blank,ncol=3,nrow=3)
+    }
+  }
+  dev.off()
+  
+  #print rank plots for L and K gene interactions
+  
+  
+  InteractionScores_AdjustMissing <- InteractionScores
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_L),]$Avg_Zscore_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_K),]$Avg_Zscore_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_r),]$Avg_Zscore_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Avg_Zscore_AUC),]$Avg_Zscore_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank <- NA
+  InteractionScores_AdjustMissing$K_Rank <- NA
+  InteractionScores_AdjustMissing$r_Rank <- NA
+  InteractionScores_AdjustMissing$AUC_Rank <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_L)
+  InteractionScores_AdjustMissing$K_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_K)
+  InteractionScores_AdjustMissing$r_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_r)
+  InteractionScores_AdjustMissing$AUC_Rank <- rank(InteractionScores_AdjustMissing$Avg_Zscore_AUC)
+  
+  #
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_L),]$Z_lm_L <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_K),]$Z_lm_K <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_r),]$Z_lm_r <- 0.001
+  InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$Z_lm_AUC),]$Z_lm_AUC <- 0.001
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- NA
+  InteractionScores_AdjustMissing$K_Rank_lm <- NA
+  InteractionScores_AdjustMissing$r_Rank_lm <- NA
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- NA
+  
+  InteractionScores_AdjustMissing$L_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_L)
+  InteractionScores_AdjustMissing$K_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_K)
+  InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r)
+  InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC)
+  
+  
+  
+  Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  
+  X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,]
+  
+  #find overlaps
+  X_NArm$Overlap <- "No Effect"
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Both")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Both")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= 2,]$Overlap <- "Deletion Enhancer lm only")
+  try(X_NArm[X_NArm$Z_lm_L <= 2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Enhancer Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= -2,]$Overlap <- "Deletion Suppressor lm only")
+  try(X_NArm[X_NArm$Z_lm_L >= -2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Suppressor Avg Zscore only")
+  try(X_NArm[X_NArm$Z_lm_L >= 2 & X_NArm$Avg_Zscore_L <= -2,]$Overlap <- "Deletion Enhancer lm, Deletion Suppressor Avg Z score")
+  try(X_NArm[X_NArm$Z_lm_L <= -2 & X_NArm$Avg_Zscore_L >= 2,]$Overlap <- "Deletion Suppressor lm, Deletion Enhancer Avg Z score")
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L <- lm(X_NArm$Z_lm_L~X_NArm$Avg_Zscore_L)
+  L_lm <- summary(get_lm_L)
+  
+  get_lm_K <- lm(X_NArm$Z_lm_K~X_NArm$Avg_Zscore_K)
+  K_lm <- summary(get_lm_K)
+  
+  get_lm_r <- lm(X_NArm$Z_lm_r~X_NArm$Avg_Zscore_r)
+  r_lm <- summary(get_lm_r)
+  
+  get_lm_AUC <- lm(X_NArm$Z_lm_AUC~X_NArm$Avg_Zscore_AUC)
+  AUC_lm <- summary(get_lm_AUC)
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm L") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_K,Z_lm_K)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm K") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(K_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_r,Z_lm_r)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm r") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(r_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(Avg_Zscore_AUC,Z_lm_AUC)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Avg Zscore vs lm AUC") +
+          geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+          annotate("text",x=0,y=0,label = paste("R-squared = ",round(AUC_lm$r.squared,2))) + theme_Publication_legend_right())
+  
+  dev.off()
+  
+  
+  lm_v_Zscore_L <- ggplot(X_NArm,aes(Avg_Zscore_L,Z_lm_L,ORF=OrfRep,Gene=Gene,NG=NG,SM=SM,DB=DB)) + geom_point(aes(color=Overlap),shape=3) + 
+    geom_smooth(method = "lm",color=1) +  ggtitle("Avg Zscore vs lm L") +
+    geom_rect(aes(xmin=-2,xmax=2,ymin=-2,ymax=2),color="grey20",size=0.25,alpha=0.1,inherit.aes = FALSE,fill=NA) +
+    annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm$r.squared,2))) + theme_Publication_legend_right()
+  
+  pgg <- ggplotly(lm_v_Zscore_L)
+  #pgg
+  plotly_path <- paste(getwd(),"/",outputpath,"Avg_Zscore_vs_lm_NA_rm.html",sep="")
+  saveWidget(pgg, file=plotly_path, selfcontained =TRUE)
+  
+  X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
+  X_NArm$K_Rank <- rank(X_NArm$Avg_Zscore_K)
+  X_NArm$r_Rank <- rank(X_NArm$Avg_Zscore_r)
+  X_NArm$AUC_Rank <- rank(X_NArm$Avg_Zscore_AUC)
+  
+  X_NArm$L_Rank_lm <- rank(X_NArm$Z_lm_L)
+  X_NArm$K_Rank_lm <- rank(X_NArm$Z_lm_K)
+  X_NArm$r_Rank_lm <- rank(X_NArm$Z_lm_r)
+  X_NArm$AUC_Rank_lm <- rank(X_NArm$Z_lm_AUC)
+  
+  #get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+  get_lm_L2 <- lm(X_NArm$L_Rank_lm~X_NArm$L_Rank)
+  L_lm2 <- summary(get_lm_L2)
+  
+  get_lm_K2 <- lm(X_NArm$K_Rank_lm~X_NArm$K_Rank)
+  K_lm2 <- summary(get_lm_K2)
+  
+  get_lm_r2 <- lm(X_NArm$r_Rank_lm~X_NArm$r_Rank)
+  r_lm2 <- summary(get_lm_r2)
+  
+  get_lm_AUC2 <- lm(X_NArm$AUC_Rank_lm~X_NArm$AUC_Rank)
+  AUC_lm2 <- summary(get_lm_AUC2)
+  
+  num_genes_NArm2 <- (dim(X_NArm)[1])/2
+  
+  pdf(paste(outputpath,"Avg_Zscore_vs_lm_ranked_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE)
+  
+  print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm L") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(K_Rank,K_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm K") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(K_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(r_Rank,r_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank Avg Zscore vs lm r") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(r_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  print(ggplot(X_NArm,aes(AUC_Rank,AUC_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) +
+          ggtitle("Rank of Avg Zscore vs lm AUC") +
+          annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(AUC_lm2$r.squared,2))) + theme_Publication_legend_right())
+  
+  
+  
+  dev.off()
+  
+  
+  
+  Rank_L_1SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Avg_Zscore_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Avg_Zscore_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext <- ggplot(X_NArm,aes(L_Rank,Avg_Zscore_L)) + 
+    ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext <- ggplot(X_NArm,aes(K_Rank,Avg_Zscore_K)) + 
+    ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  pdf(paste(outputpath,"RankPlots_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD,Rank_L_2SD,Rank_L_3SD,Rank_K_1SD,Rank_K_2SD,Rank_K_3SD,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext,Rank_L_2SD_notext,Rank_L_3SD_notext,Rank_K_1SD_notext,Rank_K_2SD_notext,Rank_K_3SD_notext,ncol=3,nrow=2)
+  
+  dev.off()
+  
+  
+  Rank_L_1SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_L >= 3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_L <= -3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -1,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 1,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -2,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 2,])[1])) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    annotate("text",x=(dim(X_NArm)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(X_NArm[X_NArm$Z_lm_K <= -3,])[1])) +
+    annotate("text",x=(dim(X_NArm)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(X_NArm[X_NArm$Z_lm_K >= 3,])[1])) + 
+    theme_Publication() 
+  
+  
+  Rank_L_1SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_2SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_L_3SD_notext_lm <- ggplot(X_NArm,aes(L_Rank_lm,Z_lm_L)) + 
+    ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  
+  Rank_K_1SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_2SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  Rank_K_3SD_notext_lm <- ggplot(X_NArm,aes(K_Rank_lm,Z_lm_K)) + 
+    ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + 
+    annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) +
+    geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + 
+    theme_Publication() 
+  
+  pdf(paste(outputpath,"RankPlots_lm_naRM.pdf",sep=""),width = 18, height = 12, onefile = TRUE)
+  
+  grid.arrange(Rank_L_1SD_lm,Rank_L_2SD_lm,Rank_L_3SD_lm,Rank_K_1SD_lm,Rank_K_2SD_lm,Rank_K_3SD_lm,ncol=3,nrow=2)
+  grid.arrange(Rank_L_1SD_notext_lm,Rank_L_2SD_notext_lm,Rank_L_3SD_notext_lm,Rank_K_1SD_notext_lm,Rank_K_2SD_notext_lm,Rank_K_3SD_notext_lm,ncol=3,nrow=2)
+  
+  dev.off()
+  
+}
+
+
+
+#get the linear model info and the r-squared value for all CPPs in results 1 vs results 2
+get_lm_1 <- lm(X_NArm$Z_lm_K~X_NArm$Z_lm_L)
+L_lm_1 <- summary(get_lm_1)
+
+get_lm_2 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_L)
+L_lm_2 <- summary(get_lm_2)
+
+get_lm_3 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_L)
+L_lm_3 <- summary(get_lm_3)
+
+get_lm_4 <- lm(X_NArm$Z_lm_r~X_NArm$Z_lm_K)
+L_lm_4 <- summary(get_lm_4)
+
+get_lm_5 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_K)
+L_lm_5 <- summary(get_lm_5)
+
+get_lm_6 <- lm(X_NArm$Z_lm_AUC~X_NArm$Z_lm_r)
+L_lm_6 <- summary(get_lm_6)
+
+
+pdf(file=paste(outputpath,"Correlation_CPPs.pdf",sep=""),width = 10, height = 7, onefile = TRUE)
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_smooth(method="lm",color="tomato3") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$Z_lm_L),]
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_K),color="cyan") +
+  ggtitle("Interaction L vs. Interaction K") + 
+  xlab("z-score L") + ylab("z-score K") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_r),color="cyan") +
+  ggtitle("Interaction L vs. Interaction r") + 
+  xlab("z-score L") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_L,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction L vs. Interaction AUC") + 
+  xlab("z-score L") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_r),color="cyan") +
+  ggtitle("Interaction K vs. Interaction r") + 
+  xlab("z-score K") + ylab("z-score r") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_K,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction K vs. Interaction AUC") + 
+  xlab("z-score K") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") +
+  geom_point(data=InteractionScores_RF2,aes(Z_lm_r,Z_lm_AUC),color="cyan") +
+  ggtitle("Interaction r vs. Interaction AUC") + 
+  xlab("z-score r") + ylab("z-score AUC") +
+  annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$r.squared,3))) +
+  theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),                                            axis.text.x = element_text(size=16),axis.title.x = element_text(size=18),                                            axis.text.y = element_text(size=16),axis.title.y = element_text(size=18))
+
+
+
+
+
+dev.off()
+
+
+#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE)
+timestamp()
+
+#BoneYard***********************************************
+#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced.
+#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. 
+#Corruptable but better than nothing.
+#if(is.na(Args[2])){
+# std=3
+#}else {
+# std= Arg[2]
+#Delta_Background_sdFactor <- 2  #Args[3]
+#DelBGFactr <- as.numeric(Delta_Background_sdFactor)
+#} 
+
diff --git a/workflow/.old/templates/qhtcp/REMcJar2.sh b/workflow/.old/templates/qhtcp/REMcJar2.sh
new file mode 100644
index 00000000..01c6d248
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/REMcJar2.sh
@@ -0,0 +1,25 @@
+
+#cp REMcRdy_lm_only.csv-finalTable.csv REMcRdy_lm_only.csv-finalTableBUP.csv
+#rm REMcRdy_lm_only.csv-finalTable.csv
+#userpath= sudo -u root -H -s eval 'echo $PWD'
+#/mnt/data/java-1.8.0-openjdk-1.8.0.352.b08-2.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+
+#/Users/anyamcdaniel/Downloads/HartmanLab/StudiesQHTCP/REMcJava/java-1.8.0-openjdk-1.8.0.342.b07-1.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+# figure out how to do this on a mac based on where REMcJava is locatedcu
+#/Users/anyamcdaniel/Downloads/HartmanLab/StudiesQHTCP/2PE_BMH21_RM_TrialOnMac/REMc/REMcJava/java-1.8.0-openjdk-1.8.0.342.b07-1.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+
+#/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.302.b08-0.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+
+#wait
+cp REMcRdy_lm_only.csv-finalTable.csv REMcRdy_lm_only.csv-finalTableBUP.csv
+rm REMcRdy_lm_only.csv-finalTable.csv
+
+current_dir="$PWD"
+
+java_executable="java" # Assuming java is available in the system's PATH
+
+classpath="$current_dir/jingyuJava_1_7_extractLib.jar"
+
+"$java_executable" -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath "$classpath" ExecMain "$current_dir/REMcRdy_lm_only.csv" "$current_dir/GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab" "$current_dir/ORF_List_Without_DAmPs.txt" 1 true true
+
+wait
diff --git a/workflow/.old/templates/qhtcp/REMcJar2old.sh b/workflow/.old/templates/qhtcp/REMcJar2old.sh
new file mode 100644
index 00000000..e3117b36
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/REMcJar2old.sh
@@ -0,0 +1,10 @@
+
+cp REMcRdy_lm_only.csv-finalTable.csv REMcRdy_lm_only.csv-finalTableBUP.csv
+rm REMcRdy_lm_only.csv-finalTable.csv
+/mnt/data/REMcJava/java-1.8.0-openjdk-1.8.0.352.b08-2.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+
+#/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.342.b07-1.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+
+#/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.302.b08-0.el7_9.x86_64/jre/bin/java -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath ./jingyuJava_1_7_extractLib.jar ExecMain ./REMcRdy_lm_only.csv GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab ORF_List_Without_DAmPs.txt 1 true true
+
+wait
diff --git a/workflow/.old/templates/qhtcp/REMcMaster2.sh b/workflow/.old/templates/qhtcp/REMcMaster2.sh
new file mode 100644
index 00000000..e6ccdd52
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/REMcMaster2.sh
@@ -0,0 +1,92 @@
+#REMcMaster.sh Is to perform all REMc task and to operate from source directory REMc
+#Step 0 Perform JoinInteractExps3dev.R to create the "REMcRdy_lm_only.csv" and "Shift_only.csv" files
+Rscript JoinInteractExps3dev.R 
+
+#Step1 Perform REMc java utility using file input file REMcRdy_lm_only.csv 
+#and producing REMcRdy_lm_only.csv-finalTable.csv
+sh ./REMcJar2.sh
+wait #wait isn't holding process until Jar is complete. Should take at least 3minuites
+
+#Step 2 
+if [ -f "REMcRdy_lm_only.csv-finalTable.csv" ]; then
+    echo "REMcRdy_lm_only.csv-finalTable.csv exists."
+    #Jarflag= 1
+    #echo "Jarflag =  $Jarflag"
+echo "REMcJar... .sh completed"
+else
+echo "REMcJar failed"
+wait
+exit
+fi
+echo "start Step2"
+#Step2 If REMcJar successfully produces "REMcRdy_lm_only.csv-finalTable.csv" 
+  #then add shift values back to the "REMcRdy_lm_only.csv-finalTable.csv"
+  #and output it as "REMcWithShift.csv" to be used to produce the REMc Heatmaps.
+if [ -f "REMcRdy_lm_only.csv-finalTable.csv" ]; then
+    echo "REMcRdy_lm_only.csv-finalTable.csv exists."
+    Rscript ./AddShiftVals2.R 
+    wait
+    echo "AddShiftVals2.R executed"
+    wait
+ fi
+
+#Step3 Execute REMcHeatmaps_zscores.R contingent upon "REMcWithShift.csv" being created.
+cd REMcHeatmaps
+if [ -f "REMcWithShift.csv" ]; then
+      echo "REMcWithShift.csv exists."
+fi
+cd ..
+      Rscript REMcHeatmaps_zscores.R
+      wait
+      echo "REMcHeatmaps executed"
+      wait
+            pdftk *.pdf output compiledREMcHeatmaps.pdf
+      wait
+    
+#Step4 Begin the process of GTF contingent upon "REMcRdy_lm_only.csv-finalTable.csv"
+# being produced by "REMcJar.sh" (Step1)
+if [ -f "REMcRdy_lm_only.csv-finalTable.csv" ]; then
+    echo "REMcRdy_lm_only.csv-finalTable.csv exists."
+     python2 DconJG2.py REMcRdy_lm_only.csv-finalTable.csv GTF/Process/
+    #python2 DconJG.py REMcRdy_lm_only.csv-finalTable.csv 12 GTF/Process/
+else
+    echo "DconJG.py failed"
+    wait
+    exit
+
+fi
+
+if [ -f "GTF/Process/REMcRdy_lm_only/1-0-0-finaltable.csv" ]; then
+  echo "Begin copying REMcRdy_lm_only to /Function and /Component"
+  cp -r GTF/Process/REMcRdy_lm_only GTF/Function/
+  echo "Copy to Function complete"
+  wait
+  cp -r GTF/Process/REMcRdy_lm_only GTF/Component/
+  echo "Copy to Component complete"
+
+  wait
+
+else
+    echo "COPY failed"
+    wait
+    exit
+
+fi
+
+if [ -f "GTF/Function/REMcRdy_lm_only/1-0-0-finaltable.csv" ]; then
+   echo "Begin concurrent running of GTF tasks"
+   sh ./mProcess.sh &
+   sh ./mFunction.sh &
+   sh ./mComponent.sh &
+
+   echo "GTF ontologies run in background"
+   wait
+else
+    echo "GTFontologies failed"
+
+    exit
+    
+    
+echo "masterDev.sh Finished"
+exit
+fi
diff --git a/workflow/.old/templates/qhtcp/REMcMaster3.sh b/workflow/.old/templates/qhtcp/REMcMaster3.sh
new file mode 100644
index 00000000..68a82f3c
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/REMcMaster3.sh
@@ -0,0 +1,121 @@
+#REMcMaster.sh Is to perform all REMc task and to operate from source directory REMc
+# Gain permissions to the perl scripts for GTF
+currentDir="$PWD"
+GTFpath="$currentDir/GTF"
+chmod -R u+rwx "$GTFpath"
+#Step 0 Perform JoinInteractExps3dev.R to create the "REMcRdy_lm_only.csv" and "Shift_only.csv" files
+Rscript JoinInteractExps3dev.R 
+
+#Step1 Perform REMc java utility using file input file REMcRdy_lm_only.csv 
+#and producing REMcRdy_lm_only.csv-finalTable.csv
+sh ./REMcJar2.sh
+wait
+#wait isn't holding process until Jar is complete. Should take at least 3minuites
+
+#Step 2 
+if [ -f "REMcRdy_lm_only.csv-finalTable.csv" ]; then
+    echo "REMcRdy_lm_only.csv-finalTable.csv exists."
+    #Jarflag= 1
+    #echo "Jarflag =  $Jarflag"
+echo "REMcJar... .sh completed"
+else
+echo "REMcJar failed"
+wait
+exit
+fi
+echo "start Step2"
+#Step2 If REMcJar successfully produces "REMcRdy_lm_only.csv-finalTable.csv" 
+  #then add shift values back to the "REMcRdy_lm_only.csv-finalTable.csv"
+  #and output it as "REMcWithShift.csv" to be used to produce the REMc Heatmaps.
+if [ -f "REMcRdy_lm_only.csv-finalTable.csv" ]; then
+    echo "REMcRdy_lm_only.csv-finalTable.csv exists."
+    Rscript ./AddShiftVals2.R 
+    wait
+    echo "AddShiftVals2.R executed"
+ fi
+
+#Step3 Execute REMcHeatmaps_zscores.R contingent upon "REMcWithShift.csv" being created.
+cd REMcHeatmaps
+if [ -f "REMcWithShift.csv" ]; then
+      echo "REMcWithShift.csv exists."
+fi
+
+rm *.pdf  #Remove .pdf files to be sure of a clean 'slate'
+
+cd ..
+      Rscript REMcHeatmaps_zscores.R
+      echo "REMcHeatmaps executed"
+      wait
+cd REMcHeatmaps
+            pdftk *.pdf output compiledREMcHeatmaps.pdf
+
+#Step3b Execute REMcHeatmaps_Z_lm_wDAmPs_andHomology_221212.R contingent upon "REMcWithShift.csv" being created in ../REMcHeatmapsWithHomology.
+#cp ./Homology/REMcWithShift.csv ./REMcHeatmapsWithHomology/REMcWithShift.csv
+cp ./REMcWithShift.csv ../REMcHeatmapsWithHomology/
+cd ../REMcHeatmapsWithHomology
+if [ -f "REMcWithShift.csv" ]; then
+      echo "REMcWithShift.csv exists."
+fi
+      rm ./Homology/*.pdf  #Remove .pdf files to be sure of a clean 'slate'
+      rm ./Homology/*.csv  #Remove .csv files to be sure of a clean 'slate'
+
+      Rscript REMcHeatmaps_Z_lm_wDAmPs_andHomology_221212.R REMcWithShift.csv Homology 17_0503_DAmPs_Only.txt Yeast_Human_Homology_Mapping_biomaRt_18_0920.csv
+    
+      echo "REMcHeatmaps_Z_lm_wDAmPs_andHomology_221212.R executed"
+      wait
+cd Homology
+      pdftk *.pdf output compiledREMcHomologyHeatmaps.pdf
+      
+echo "pdftk executed"
+cd ../..
+    
+#Step4 Begin the process of GTF contingent upon "REMcRdy_lm_only.csv-finalTable.csv"
+# being produced by "REMcJar.sh" (Step1)
+if [ -f "REMcRdy_lm_only.csv-finalTable.csv" ]; then
+    echo "REMcRdy_lm_only.csv-finalTable.csv exists."
+     python3 DconJG2.py REMcRdy_lm_only.csv-finalTable.csv GTF/Process/
+    #python2 DconJG.py REMcRdy_lm_only.csv-finalTable.csv 12 GTF/Process/
+else
+    echo "DconJG2.py failed"
+    wait
+    exit
+
+fi
+
+if [ -f "GTF/Process/REMcRdy_lm_only/1-0-0-finaltable.csv" ]; then
+  echo "Begin copying REMcRdy_lm_only to /Function and /Component"
+  cp -r GTF/Process/REMcRdy_lm_only GTF/Function/
+  echo "Copy to Function complete"
+  wait
+  cp -r GTF/Process/REMcRdy_lm_only GTF/Component/
+  echo "Copy to Component complete"
+
+  wait
+
+else
+    echo "COPY failed"
+    wait
+    exit
+
+fi
+
+if [ -f "GTF/Function/REMcRdy_lm_only/1-0-0-finaltable.csv" ]; then
+   echo "Begin concurrent running of GTF tasks"
+   sh ./mProcess.sh &
+   sh ./mFunction.sh &
+   sh ./mComponent.sh &
+
+   echo "GTF ontologies run in background"
+
+else
+    echo "GTFontologies failed"
+    
+fi
+
+wait
+
+Rscript CompileGTF.R
+wait 
+
+echo "REMcMaster3.sh Finished"
+    
diff --git a/workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv b/workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv
new file mode 100644
index 00000000..e37a9e80
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv
@@ -0,0 +1,832 @@
+OrfRep,Gene,FY4_Z_lm_K,BY4742_Z_lm_K_1,FY4_Z_lm_L,BY4742_Z_lm_L_1
+KML14-13_-6_,VPS8,-59.3847278181521,-6.06157169672863,0.772743415948687,8.39820521641744
+KML12-19MG-C1DNMT,VPS8,-61.7704731480934,-7.00152259748668,3.59681346254071,8.99468364704015
+CQ10-30A-4CONTROL,VPS8,-22.1493418068443,1.01057802879191,-1.32259812578781,-1.40227673581777
+NBP13-2_-7_,VPS8,-51.1031045178275,-3.73433070180383,4.71147533646182,8.19513739350017
+CQ10-27C-1HDAC,VPS8,-26.4720466459974,-0.331443770649795,3.06034059896547,0.705141331827187
+PSC03-61_-6_,VPS8,-25.3220571929659,-0.72622940144637,2.6849735823816,1.68355485772886
+EG12-32D-3AHDAC,VPS8,-41.324395003364,-4.09835822552595,2.32241487629303,3.03853435965829
+CQ10-06B-1CONTROL,VPS8,-39.0716102878534,-5.34095716930887,2.01966216347308,3.77845508412703
+EG12-30A-6CONTROL,VPS8,-37.8775642922419,-1.76184832277848,1.49657970926092,1.19181087929409
+CQ10-30A-4DNMT,VPS8,-35.4936750499954,0.522291578085531,-0.327827611198834,0.87541470554711
+Lys_3,VPS8,-39.8518983279553,1.52099978286979,4.48531893673844,-2.86418009670572
+PSC07-4_-7__2,VPS8,-7.23278458251803,-3.71137209074474,3.57258904477228,8.02246684078295
+PSC10-14_-6_,VPS8,-15.1064754799911,0.75356131177783,3.54251842817478,1.3233468188676
+PSC03-17_-6_,VPS8,-8.18234185181037,-3.62751532438468,3.51942828772159,8.07587628895911
+Farnesol_1,VPS8,-13.1295215797973,-1.94911804017062,3.39747359540882,0.968785243900746
+PSC08-79_-7_,VPS8,-4.19181905498484,-3.47276039612587,3.36530291687454,3.9612329984847
+PSC11-37-M37A-1_-6_,VPS8,-5.58451903151947,-3.35601750693486,3.33705911091163,7.50674629779757
+Leucine_2,VPS8,-19.9462802819614,-2.18975358413756,3.29533708404183,0.337399876432149
+Tryptophan_2,VPS8,-29.1403100346624,-6.86896685809796,3.28961619940997,-1.79505709927979
+KML12-33_-7_,VPS8,-5.77846047153248,-1.30037937389063,3.23000650860923,1.70474451122617
+Tryptophan_1,VPS8,-14.1189265737416,-4.75429893154531,3.22587287318308,8.79792713362979
+Lys_1,VPS8,-11.2856668684289,1.10570501845037,3.19620576755786,-2.89936157176371
+PSC04-37_-7__1,VPS8,-19.2630326544322,-4.01291286825037,3.12775096546675,2.17147652385328
+BGC13-12_-6_,VPS8,-4.69555012893993,0.129023264065035,3.11114352910146,1.03744784040905
+PSC08-33_-7_,VPS8,-14.159063585086,1.01346543098873,3.10783804064975,0.058814946877905
+CWR15-17_-7__1,VPS8,-7.02325099437684,-0.523296278142047,3.09577687412093,2.54467584732745
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+14.317g,VPS8,1.88504037953566,0.622862244364823,-3.28937740257604,-0.276931985907223
+EG12-27E-2CONTROL,VPS8,3.15339765289825,-2.16531020939492,-3.30878067753635,1.82190745185149
+CQ10-29B-1CONTROL,VPS8,-0.518035853164554,-2.20553741513009,-3.58666184601311,1.54460313221827
+EG12-35E-2DNMT,VPS8,-1.4012038495154,0.374674382930679,-3.64216753708742,-0.0319739872593032
+KML12-19MG-C5CONTROL,VPS8,-2.27855374427475,-0.3393096257977,-3.78002322446424,-0.719899902686996
+CQ10-29C-4HDAC,VPS8,-1.96255860733129,0.370201473795531,-3.87293272138557,-2.13354046901214
+SCXIVI93-638_-7_,VPS8,0.95333897731479,-5.87927361475361,-3.89817562803265,8.40089739610227
+CQ10-29C-4CONTROL,VPS8,0.335012947934112,0.784308906997897,-4.01122058367063,-3.58349169799806
+EG12-40A-4DNMT,VPS8,-1.68579519645332,0.290298937296287,-4.02835352140198,0.224717194803488
+EG11-10A-1CONTROL,VPS8,-0.43422363169839,0.524861330632072,-4.0906223849144,-0.237481015349567
+SC95-1624_-6_,VPS8,3.61372965402897,-3.98611402375268,-4.10746909798796,7.889393906364
+NBP13-46_-7_,VPS8,2.25260744411216,0.816739211548377,-4.11771399473362,-0.523971700389474
+EG11-10A-1HDAC,VPS8,1.12117785653664,-6.42086542968985,-4.29911961517014,2.99064731445993
+KML12-17MG-C1DNMT,VPS8,1.5870164197439,-6.997035296458,-4.56523232845366,8.96011093062638
+EG12-35E-2HDAC,VPS8,1.31270789978432,0.094811334471397,-5.4585224578969,0.0336607668659672
+CQ10-30A-4HDAC,VPS8,23.3944557567319,1.25783452911554,-10.617064327657,-0.488593444526053
+EG12-39B-1CONTROL,VPS8,25.3485438703162,-2.53052449671489,-13.9901457012852,0.502529665910034
+EG12-30A-2CONTROL,VPS8,10.4352332648449,0.0144430031955158,-18.4053378097147,-1.2514717022687
diff --git a/workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv-WholeTree.txt b/workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv-WholeTree.txt
new file mode 100644
index 00000000..e74e5e03
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv-WholeTree.txt
@@ -0,0 +1,3 @@
+root cluster is:0-0-0
+root cluster is:0-0-0
+root cluster is:0-0-0
diff --git a/workflow/.old/templates/qhtcp/mComponent.sh b/workflow/.old/templates/qhtcp/mComponent.sh
new file mode 100644
index 00000000..7ee35b8c
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/mComponent.sh
@@ -0,0 +1,32 @@
+#This is a 'master associated' sh script to operate from the REMc source directory
+#It is an altered Process.sh with a cd at start 
+
+cd GTF/Component
+#Begin the Original Component.sh
+#!/bin/bash
+# this one code will load the terms2tsv_v4.pl to add two more columns: one is the gene names, the other one is the cluster name column
+
+set $1 = ORF_List_Without_DAmPs.txt #ORF_List_Without_DAmPs.txt #ORFs_w_DAmP_list.txt
+set $2 = REMcRdy_lm_only/*.txt
+#paths= ORF_List_Without_DAmPs.txt:REMcRdy_lm_only/*.txt
+#if [ $argv -lt 2 ]; then
+	
+#echo "Usage: ./example_v4.sh backgroundFilePath geneListsFolder"
+
+#else
+	for i in ${*:2}       
+	do
+		#echo $1
+		#echo $i
+		./analyze_v2.pl -an gene_association.sgd -as C -o gene_ontology_edit.obo -b $1 $i
+		./terms2tsv_v4.pl $i.terms > $i.tsv
+	done
+#fi
+wait
+echo "Component complete"
+#Concatinatethe Process ontology outputs from the /REMcRdy_lm_only folder
+python3 Concatenate_GTF_results.py REMcRdy_lm_only/ ComponentResults.txt
+wait
+echo "Component Concatenate complete"
+pwd
+#exit
diff --git a/workflow/.old/templates/qhtcp/mFunction.sh b/workflow/.old/templates/qhtcp/mFunction.sh
new file mode 100644
index 00000000..671ed258
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/mFunction.sh
@@ -0,0 +1,32 @@
+#This is a 'master associated' sh script to operate from the /REMc source directory
+#It is an altered Function.sh with a cd at start 
+
+cd GTF/Function
+#Begin the Original Function.sh
+#!/bin/bash
+# this one code will load the terms2tsv_v4.pl to add two more columns: one is the gene names, the other one is the cluster name column
+
+set $1 = ORF_List_Without_DAmPs.txt #ORF_List_Without_DAmPs.txt #ORFs_w_DAmP_list.txt
+set $2 = REMcRdy_lm_only/*.txt
+
+#if [ $argv -lt 2 ]; then
+#echo "Usage: ./example_v4.sh backgroundFilePath geneListsFolder"
+#else
+
+
+	for i in ${*:2}       
+	do
+		#echo $1
+		#echo $i
+		./analyze_v2.pl -an gene_association.sgd -as F -o gene_ontology_edit.obo -b $1 $i
+		./terms2tsv_v4.pl $i.terms > $i.tsv
+	done
+wait
+echo "Function complete"
+#fi
+#Concatinatethe Process ontology outputs from the /REMcRdy_lm_only folder
+python3 Concatenate_GTF_results.py REMcRdy_lm_only/ FuctionResults.txt
+wait
+echo "FunctionConcatenate complete"
+pwd
+#exit
diff --git a/workflow/.old/templates/qhtcp/mProcess.sh b/workflow/.old/templates/qhtcp/mProcess.sh
new file mode 100644
index 00000000..30a0bf55
--- /dev/null
+++ b/workflow/.old/templates/qhtcp/mProcess.sh
@@ -0,0 +1,37 @@
+#This is a 'master associated' sh script to operate from the REMc source directory
+#It is an altered Process.sh with a cd at start 
+
+cd GTF/Process
+#Begin the Original Process.sh
+#!/bin/bash
+# this one code will load the terms2tsv_v4.pl to add two more columns: one is the gene names, the other one is the cluster name column
+
+set $1 = ORF_List_Without_DAmPs.txt #ORFs_w_DAmP_list.txt #ORF_List_Without_DAmPs.txt
+set $2 = REMcRdy_lm_only/*.txt
+#paths= ORF_List_Without_DAmPs.txt:REMcRdy_lm_only/*.txt
+#if [ $argv -lt 2 ]; then
+	
+#echo "Usage: ./example_v4.sh backgroundFilePath geneListsFolder"
+
+#else
+	for i in ${*:2}       
+	do
+		#echo $1
+		#echo $i
+		./analyze_v2.pl -an gene_association.sgd -as P -o gene_ontology_edit.obo -b $1 $i
+		./terms2tsv_v4.pl $i.terms > $i.tsv
+	done
+#fi
+wait
+echo "Process complete"
+#Concatinatethe Process ontology outputs from the /REMcReady_lm_only folder
+#python2 Concatenate_GTF_results.py ./REMcReady_lm_only/ ProcessResultsTest.txt
+pwd 
+python3 Concatenate_GTF_results.py REMcRdy_lm_only/ ProcessResults.txt
+#python2 ConcatGTFdebug.py REMcRdy_lm_only/ ProcessResults.txt
+#python2 ConcatGTFdebug.py REMcRdy_lm_only/ PresDebug4.txt
+
+wait
+echo "Process Concatenate complete"
+pwd
+#exit
diff --git a/workflow/templates/easy/DMPexcel2mat.m b/workflow/apps/easy/DMPexcel2mat.m
similarity index 93%
rename from workflow/templates/easy/DMPexcel2mat.m
rename to workflow/apps/easy/DMPexcel2mat.m
index 4fddde53..238b02ef 100755
--- a/workflow/templates/easy/DMPexcel2mat.m
+++ b/workflow/apps/easy/DMPexcel2mat.m
@@ -7,7 +7,7 @@ global matDir
 
 % If we already have mpdmFile, don't recreate
 if (exist(mpdmFile, 'file') && ~isempty(mpdmFile))
-  disp(sprintf('The Drug Media/MasterPlate Annotation File: %s exists, skipping DMPexcel2mat.m\n', mpdmFile));
+  fprintf('The Drug Media/MasterPlate Annotation File: %s exists, skipping DMPexcel2mat.m\n', mpdmFile);
   return
 end
 
@@ -82,7 +82,7 @@ if ~exist(masterPlateFile, 'file') || isempty(masterPlateFile)
     end
   end
 else
-  disp(sprintf('Using MasterPlate file: %s skipping directory selection\n', masterPlateFile));
+  fprintf('Using MasterPlate file: %s skipping directory selection\n', masterPlateFile);
 end
 
 % fid=fopen(masterPlateFile)%('exp23PrintTimes.xls'); % textread puts date and time sequentially into vector
@@ -175,7 +175,7 @@ numOfMedias=0;
 % find a matching DrugMedia file
 [mpFile, mpPath]=fullfile(masterPlateFile);
 mpFileParts=strsplit(mpFile, '_');
-mpBareFileName=strjoin(parts(2:end-1), '_');
+mpBareFileName=strjoin(mpFileParts(2:end-1), '_');
 
 if ~exist(drugMediaFile, 'file') || isempty(drugMediaFile)
   if exist(fullfile(matDir), 'dir')
@@ -183,7 +183,7 @@ if ~exist(drugMediaFile, 'file') || isempty(drugMediaFile)
       dmFileToTest=fullfile(mpPath, 'DrugMedia_', mpBareFileName, '.xlsx');
       if exist(dmFileToTest, 'file') % Try to find a matching drug media file
         drugMediaFile=dmFileToTest;
-        disp(sprintf('Using matching DrugMedia file: %s, skipping directory selection\n', drugMediaFile));
+        fprintf('Using matching DrugMedia file: %s, skipping directory selection\n', drugMediaFile);
       else
         % Try to find the DrugMedia file automatically (newest created first)
         files=dir(matDir);
@@ -195,7 +195,7 @@ if ~exist(drugMediaFile, 'file') || isempty(drugMediaFile)
         [~, sortedIndices]=sort(datenum({files(strncmp(dmFiles.name, 'DrugMedia_', 10)).date}), 'descend');
         sortedFiles=dmFiles{sortedIndices};
         drugMediaFile=sortedFiles{1};
-        disp(sprintf('Using newest DrugMedia file: %s, skipping directory selection\n', drugMediaFile));
+        fprintf('Using newest DrugMedia file: %s, skipping directory selection\n', drugMediaFile);
 
       end
     catch Me
@@ -229,7 +229,7 @@ if ~exist(drugMediaFile, 'file') || isempty(drugMediaFile)
     end
   end
 else
-  disp(sprintf('Using drugMediaFile: %s, skipping directory selection\n', drugMediaFile));
+  fprintf('Using drugMediaFile: %s, skipping directory selection\n', drugMediaFile);
 end
 
 % Drug and Media Plate setup
diff --git a/workflow/templates/easy/DgenResults.m b/workflow/apps/easy/DgenResults.m
similarity index 96%
rename from workflow/templates/easy/DgenResults.m
rename to workflow/apps/easy/DgenResults.m
index ba1e42a7..403bcb16 100755
--- a/workflow/templates/easy/DgenResults.m
+++ b/workflow/apps/easy/DgenResults.m
@@ -366,7 +366,7 @@ if isequal(opt,'DB')||isequal(opt,'Both')
   try
     copyfile(DBfilename,DBupload)
   catch ME
-    disp(sprintf('DB upload failed with error: %s\n', getReport(ME, 'basic')));
+    fprintf('DB upload failed with error: %s\n', getReport(ME, 'basic'));
     rep=sprintf('Failed copyfile to %s - %s', DBupload, rep);
     errordlg(rep);
   end
@@ -654,7 +654,7 @@ try
     try
       copyfile(DBfilename,DBupload)
     catch ME
-      disp(sprintf('DB upload failed with error: %s\n', getReport(ME, 'basic')));
+      fsprintf('DB upload failed with error: %s\n', getReport(ME, 'basic'));
       rep=sprintf('Failed copyfile to %s - %s\n', DBupload, rep);
       errordlg(rep)
     end
@@ -663,6 +663,6 @@ try
   msgbox([sprintf('Printing Script complete. Check !!Results sheets in %s for results.', printResultsDir)])
 
 catch ME
-  disp(sprintf('Printing Script failed with error: %s\n', getReport(ME, 'basic')));
+  fprintf('Printing Script failed with error: %s\n', getReport(ME, 'basic'));
 end
 
diff --git a/workflow/templates/easy/EASYconsole.fig b/workflow/apps/easy/EASYconsole.fig
similarity index 100%
rename from workflow/templates/easy/EASYconsole.fig
rename to workflow/apps/easy/EASYconsole.fig
diff --git a/workflow/templates/easy/EASYconsole.m b/workflow/apps/easy/EASYconsole.m
similarity index 74%
rename from workflow/templates/easy/EASYconsole.m
rename to workflow/apps/easy/EASYconsole.m
index 4c47c498..d00d8291 100644
--- a/workflow/templates/easy/EASYconsole.m
+++ b/workflow/apps/easy/EASYconsole.m
@@ -1,6 +1,5 @@
 % Launch the MATLAB EASY console
-%
-% Updated 240724 Bryan C Roessler to improve file operations and portability
+% Updated 240727 Bryan C Roessler to improve file operations and portability
 % 
 function varargout = EASYconsole(varargin)
   global easyDir
@@ -27,122 +26,134 @@ function varargout = EASYconsole(varargin)
   [easyDir,easyFileName]=fileparts(easyPath);
   easyDir=fullfile(easyDir);
   [parentDir, ~]=fileparts(easyDir);
-  userName=system('whoami');
-  todayStr=datetime('now', 'Format', 'yyyyMMdd'); % This should match the parent workflow script
+  parentDir=fullfile(parentDir); % ../easy/apps
+  userName=getenv('USER');
+  dt=datetime;
+  todayStr=char(dt, 'yyyyMMdd'); % This should match the parent workflow script
 
-  disp(sprintf('This script name: %s\n', easyFileName))
+  demo=1;
+  if demo
+    disp('Running in demo mode');
+    disp('Initialized variables:');
+    whos;
+  end
 
-  % Set scansDir intelligently (project scans directory)
-  % Allow users to specify a PROJECT=/mnt/data/ExpJobs/Job directory to analyze with EASY
-  % This better enables running the new EASY in standalone mode
+  fprintf('This script name: %s\n', easyFileName);
+
+  % Set scansDir (project scans directory) intelligently
   if exist('PROJECT', 'var') && ~isempty(getenv('PROJECT'))
-    scansDir=getenv('PROJECT') 
-    disp(sprintf('Using project path: %s from environment variable PROJECT\n', scansDir));
+    scansDir=getenv('PROJECT');
+    fprintf('Using project path: %s from environment variable PROJECT\n', scansDir);
     disp('This usually indicates that we are in standalone mode');
   elseif exist('SCANS_DIR', 'var') && ~isempty(getenv('SCANS_DIR'))
     scansDir=getenv('SCANS_DIR');
-    disp(sprintf('Using scans directory: %s from environment variable SCANS_DIR\n', scansDir));
+    fprintf('Using scans directory: %s from environment variable SCANS_DIR\n', scansDir);
     disp('This usually indicates that we are in module mode');
   else
-    dirToScan = fullfile(parentDir,'ExpJobs'); % hardcoded relative to easy script dir TODO: if we change pj layout this will change
+    dirToScan=fullfile(parentDir,'..','ExpJobs'); % hardcoded relative to easy script dir TODO: if we change pj layout this will change
     if exist(dirToScan, 'dir')
       dirs=dir(fullfile(dirToScan, 'dir')); % filter for dirs only
       [~, sortedIndices]=sort(datenum({dirs.date}), 'descend'); % sort by newest first
       sortedDirs=dirs{sortedIndices};
       scansDir=sortedDirs{1};
       disp('Beginning in newest project scans directory');
-      disp(sprintf('Using scans directory: %s from hardcoded relative path\n', scansDir));
+      fprintf('Using scans directory: %s from hardcoded relative path\n', scansDir);
       disp('This usually indicates that we are in stand-alone mode without PROJECT or SCANS_DIR environment variables');
     else
-      scansDir=fullfile('/mnt/data/ExpJobs/demo')
+      scansDir=fullfile(parentDir, '..', 'demo', '20240727_hartmanlab_demo_project');
       if exist(scansDir, 'dir')
-        disp(sprintf('Using scans directory: %s from hardcoded absolute path to demo\n', scansDir));
+        demo=1;
+        fprintf('Using scans directory: %s from hardcoded absolute path to demo project\n', scansDir);
       else
-        disp(sprintf('Error: scansDir %s does not exist\n', scansDir));
+        fprintf('Error: demo project %s not found\n', scansDir);
+        disp('Attempting to continue but this may get ugly');
       end
     end
   end
 
-  % If we don't have the EASY_SUFFIX from the module, generate it from scansDir
-  if exist('EASY_SUFFIX', 'var') && ~isempty(getenv('EASY_SUFFIX'))
-    easySuffix=get_env('EASY_SUFFIX');
-  else
-    [dirName, ~]=fileparts(scansDir);
-    [scansDate, scansUserName, easySuffix]=strsplit(dirName, '_');
-    % The following is handled by the workflow script that calls this module
-    % Reimplementing here for stand-alone mode
-    % While the easySuffix directory exists, increment by one and try again
-    if exist(easySuffix, 'dir')
-      oldSuffix=easySuffix;
-      while exist(easySuffix, 'dir')
-        count=1;
-        easySuffix=strcat(oldSuffix,'.',num2str(count));
-      end
-    end
-    % Might as well check this too
-    if userName ~= scansUserName
-      disp('WARNING: userName does not match scansUserName');
-      disp("This usually means that you are attempting to run an EASY analysis on another user's project data scans");
-    end
-    % For happiness
-    if todayStr == scansDate
-      disp("Early bird gets the worm");
-    end
-  end
-
-  % Helpful variables for running in workflow mode that I'll probably have to reimplement in matlab anyways for standalone mode
-  if exist('EASY_DIR','var') && ~isempty(getenv('EASY_DIR'))
-    EASY_DIR=fullfile(get_env('EASY_DIR'));
-    if easyDir ~= EASY_DIR % sanity check
-      disp("WARNING: EASY_DIR does not match this script's hardcoded EASY location");
-      disp("This is probably OK but if strange beahvior arises, we'll need to fix it in code");
-      easyDir=EASY_DIR;
-    end
-    disp(sprintf('Using EASY script directory: %s from environment variable EASY_DIR\n', easyDir));
-  else
-    disp(sprintf('Using EASY script directory: %s from hardcoded default\n', easyDir));
-  end
-
+  % Sanity check and warning
   if exist('PROJECT_USER', 'var') && ~isempty(getenv('PROJECT_USER'))
-    if get_env('PROJECT_USER') ~= userName % sanity check
+    if ~equal(getenv('PROJECT_USER'), userName)
       disp("WARNING: PROJECT_USER does not match the current namespace");
     end
   end
 
-  if exist('EASY_RESULTS_DIR', 'var') && ~isempty(getenv('EASY_RESULTS_DIR'))
-    easyResultsDir=fullfile(get_env('EASY_RESULTS_DIR'));
-    disp(sprintf('Using output directory: %s from environment variable EASY_RESULTS_DIR\n', easyResultsDir))
+  % Allow module to override hardcoded default EASY directory
+  if exist('EASY_DIR','var') && ~isempty(getenv('EASY_DIR'))
+    EASY_DIR=fullfile(getenv('EASY_DIR'));
+    if ~strcmp(easyDir, EASY_DIR) % sanity check
+      disp("WARNING: EASY_DIR does not match this script's hardcoded EASY location");
+      disp("This is probably OK but if strange beahvior arises, we'll need to fix it in code");
+      easyDir=EASY_DIR;
+    end
+    fprintf('Using EASY script directory: %s from environment variable EASY_DIR\n', easyDir);
+  else
+    fprintf('Using EASY script directory: %s from hardcoded default\n', easyDir);
+  end
+
+  % If we don't have tan EASY_SUFFIX from the module, generate it from scansDir
+  if exist('EASY_SUFFIX', 'var') && ~isempty(getenv('EASY_SUFFIX'))
+    easySuffix=getenv('EASY_SUFFIX');
+  else
+    % The following is a way to parse the project name from the scansDir
+    [ ~, dirName]=fileparts(scansDir);
+    parts=strsplit(dirName, '_');
+    scansDate=parts{1};
+    scansUserName=parts{2};
+    easySuffix=strjoin(parts(3:end), '_');
+    % Might as well check this too for fun
+    if ~strcmp(userName, scansUserName)
+      disp('WARNING: userName does not match scansUserName');
+      disp("This usually means that you are attempting to run an EASY analysis on another user's project data scans");
+    end
+    % For happiness
+    if strcmp(todayStr, scansDate)
+      disp("Early bird gets the worm");
+    end
+  end
+
+  if (exist('EASY_RESULTS_DIR', 'var') && ~isempty(getenv('EASY_RESULTS_DIR')))
+    easyResultsDir=fullfile(getenv('EASY_RESULTS_DIR'));
+    if exist(easyResultsDir, 'dir')
+      fprintf('WARNING: EASY results dir %s already exists\n', easyResultsDir);
+      disp('Files in this directory may be overwritten');
+    end
+    fprintf('Using output directory: %s from environment variable EASY_RESULTS_DIR\n', easyResultsDir);
   else
     easyResultsDirName=strcat('Results_',todayStr,'_',userName,'_',easySuffix);
     easyResultsDir=fullfile(scansDir,easyResultsDirName);
-    disp(sprintf('Using output directory: %s\n', PROJECT_PREFIX))
+    if exist(easyResultsDir, 'dir')
+      fprintf('WARNING: EASY results dir %s already exists\n', easyResultsDir);
+      disp('Files in this directory may be overwritten')
+      fprintf('Using output directory: %s\n', easyResultsDir);
+    end
   end
 
   if exist('MASTER_PLATE_FILE', 'var') && ~isempty(getenv('MASTER_PLATE_FILE'))
-    masterPlateFile=fullfile(get_env('MASTER_PLATE_FILE'));
-    disp(sprintf('Using drug media file: %s from environment variable MASTER_PLATE_FILE\n', masterPlateFile))
+    masterPlateFile=fullfile(getenv('MASTER_PLATE_FILE'));
+    fprintf('Using drug media file: %s from environment variable MASTER_PLATE_FILE\n', masterPlateFile);
   else
     % Try to find MasterPlate_ file on our own
     mp=fullfile(scansDir,'MasterPlateFiles',strcat('MasterPlate_', easySuffix,'.xlsx'));
     if exist(mp, 'file')
       masterPlateFile=mp;
-      disp(sprintf('Using drug media file: %s from internal logic\n', masterPlateFile))
+      fprintf('Using drug media file: %s from internal logic\n', masterPlateFile);
     else
-      disp("WARNING: Have you created a MasterPlate_ file?")
+      fprintf('WARNING: Have you created a MasterPlate_ file in %s/MasterPlateFiles/?\n', scansDir);
     end
   end
 
   if exist('DRUG_MEDIA_FILE', 'var') && ~isempty(getenv('DRUG_MEDIA_FILE'))
-    drugMediaFile=fullfile(get_env('DRUG_MEDIA_FILE'));
-    disp(sprintf('Using drug media file: %s from environment variable DRUG_MEDIA_FILE\n', drugMediaFile))
+    drugMediaFile=fullfile(getenv('DRUG_MEDIA_FILE'));
+    fprintf('Using drug media file: %s from environment variable DRUG_MEDIA_FILE\n', drugMediaFile);
   else
     % Try to find MasterPlate_ file on our own
     dm=fullfile(scansDir,'MasterPlateFiles',strcat('DrugMedia_', easySuffix,'.xlsx'));
     if exist(mp, 'file')
       drugMediaFile=dm;
-      disp(sprintf('Using drug media file: %s from internal logic\n', drugMediaFile))
+      fprintf('Using drug media file: %s from internal logic\n', drugMediaFile);
     else
-      disp("WARNING: Have you created a DrugMedia_ file?")
+      fprintf('WARNING: Have you created a DrugMedia_ file in %s/MasterPlateFiles/?\n', scansDir);
     end
   end
 
@@ -161,6 +172,12 @@ function varargout = EASYconsole(varargin)
   searchRangeFile=fullfile(fotosResultsDir,'CSearchRange.mat');
   mpdmFile=fullfile(matDir,'MPDM.mat');
 
+  % Decent time to print some helpful vars
+  if demo
+    disp('Vars at end of main loop:')
+    whos;
+  end
+
   % This can be removed, I think it should add the previous search range?
   % Might be nice feature but can remove if it causes issues
   % We are using searchRangeNum to hold old CSrchRange value(s)
@@ -172,14 +189,6 @@ function varargout = EASYconsole(varargin)
   % reduce directory scoping issues when calling scripts w/o a path
   addpath(easyDir);
 
-  % Pulled this out of the opening function
-  % Seems better to wait until we have our ars set
-  if exist('scansDir','var') && ~isempty(scansDir)
-    set(fhconsole,'Name',sprintf('EASYconsole - %s', scansDir));
-  else
-    set(fhconsole,'Name','EASYconsole - No Active Experiment.')
-  end
-
   % GUI interface design
   gui_Singleton=1;
   gui_State=struct( 'gui_Name',       mfilename, ...
@@ -221,6 +230,7 @@ end
 % varargin--input arguments to EASYconsole (see VARARGIN)
 function EASYconsole_OpeningFcn(hObject, ~, handles, varargin)
   global fhconsole
+  global scansDir
 
   % Choose default command line output for EASYconsole
   handles.output=hObject;
@@ -232,6 +242,15 @@ function EASYconsole_OpeningFcn(hObject, ~, handles, varargin)
   fhconsole=gcf;
   set(fhconsole,'Toolbar','none');
   fhconsole=gcf;
+
+  % Pulled this out of the opening function
+  % Seems better to wait until we have our vars set though?
+  if exist('scansDir','var') && ~isempty(scansDir)
+    set(fhconsole,'Name', sprintf('EASYconsole - %s', scansDir));
+  else
+    set(fhconsole,'Name','EASYconsole - No Active Experiment.')
+  end
+
 end
 
 
@@ -319,7 +338,7 @@ function NewExpDat_Callback(~, ~, ~)
     sbdg= cell(1,scanMax); 
     save((fullfile(easyResultsDir,'Fotos','Nbdg')),'sbdg'); 
   catch ME
-    disp(sprintf('ERROR: %s\n', ME.message));
+    fprintf('ERROR: %s\n', ME.message);
   end
 
   % set the title for fhconsole depending on existence
@@ -340,7 +359,7 @@ function LoadDatFile_Callback(~, ~, ~)
   global fhconsole
 
   try
-    questdlg('\fontsize{20} Load file from ExpJobs/YourJob/YourResults/matResults','File Creation','OK', struct('Default','OK','Interpreter','tex'));
+    questdlg('Load results .mat from ../ExpJobs/YourJob/Results/matResults/','File Creation','OK', struct('Default','OK','Interpreter','tex'));
     [inputFile,inputPath]=uigetfile('.mat','Open Experiment folder and data storage .mat file name','MultiSelect','off');
     matDir=fullfile(inputPath);
     matFile=fullfile(inputPath,inputFile);
@@ -368,9 +387,11 @@ function LoadDatFile_Callback(~, ~, ~)
         try
           load(fullfile(easyPath,'ImParameters.mat'));
         catch
-          disp("Could not load the ImParameters.mat file")
+          disp("Could not load the ImParameters.mat file");
         end
-      end  
+      end
+    else
+      disp('WARNING: cannot find project scans');
     end
 
     bkupDir=fullfile(matDir,'BkUp');
@@ -395,7 +416,7 @@ function LoadDatFile_Callback(~, ~, ~)
     fhconsole=gcf;
     set(fhconsole,'Name',sprintf('EASYconsole - %s', easyResultsDir));
   else
-    set(fhconsole,'Name','EASYconsole -Exp. Analysis NOT selected.');
+    set(fhconsole,'Name','EASYconsole - Exp. Analysis NOT selected.');
   end
 end
 
@@ -415,6 +436,7 @@ end
 function NImCFcombo_Callback(~, ~, ~)
   try   
     par4Gbl_Main8c
+    EASYconsole
   catch 
     EASYconsole
   end
@@ -433,6 +455,7 @@ function PlateCFit_Callback(~, ~, ~)
   try
     NCstart  
   catch
+    EASYconsole
   end
 end
 
@@ -455,7 +478,7 @@ function runResults_DBcombo_Callback(~, ~, ~)
     DgenResults %similar but semicolons removed to restore so cmdLine display info.
     %Dgen241010qhtcp %par4global -convert 1x1cell of 384cells to be like previous 1x384 cells CFparameter
   catch ME
-    disp(sprintf('Error in DgenResults: %s\n', ME.message));
+    fprintf('Error in DgenResults: %s\n', ME.message);
     EASYconsole
   end
 end
diff --git a/workflow/templates/easy/NCdisplayGui.m b/workflow/apps/easy/NCdisplayGui.m
similarity index 100%
rename from workflow/templates/easy/NCdisplayGui.m
rename to workflow/apps/easy/NCdisplayGui.m
diff --git a/workflow/templates/easy/NCfitImCFparforFailGbl2.m b/workflow/apps/easy/NCfitImCFparforFailGbl2.m
similarity index 100%
rename from workflow/templates/easy/NCfitImCFparforFailGbl2.m
rename to workflow/apps/easy/NCfitImCFparforFailGbl2.m
diff --git a/workflow/templates/easy/NCscurImCF_3parfor.m b/workflow/apps/easy/NCscurImCF_3parfor.m
similarity index 100%
rename from workflow/templates/easy/NCscurImCF_3parfor.m
rename to workflow/apps/easy/NCscurImCF_3parfor.m
diff --git a/workflow/templates/easy/NCsingleDisplay.m b/workflow/apps/easy/NCsingleDisplay.m
similarity index 100%
rename from workflow/templates/easy/NCsingleDisplay.m
rename to workflow/apps/easy/NCsingleDisplay.m
diff --git a/workflow/templates/easy/NIcircle.m b/workflow/apps/easy/NIcircle.m
similarity index 100%
rename from workflow/templates/easy/NIcircle.m
rename to workflow/apps/easy/NIcircle.m
diff --git a/workflow/templates/easy/NImParamRadiusGui.m b/workflow/apps/easy/NImParamRadiusGui.m
similarity index 94%
rename from workflow/templates/easy/NImParamRadiusGui.m
rename to workflow/apps/easy/NImParamRadiusGui.m
index a096e980..37b4958a 100755
--- a/workflow/templates/easy/NImParamRadiusGui.m
+++ b/workflow/apps/easy/NImParamRadiusGui.m
@@ -41,7 +41,7 @@ function NImParamRadiusGui(scansDir)
   if exist(pointMapsFile, 'file')
     load(pointMapsFile);
   else
-    load(fullfile(PTmats,'NImParameters')) % hardcoded default
+    load(fullfile(easyDir, 'PTmats', 'NImParameters')) % hardcoded default
     disp('WARNING: Using hardcoded NImParameters.mat')
   end
   ImParMat;
@@ -67,7 +67,7 @@ function NImParamRadiusGui(scansDir)
     set(fhImParm,'Name',strcat('ImageAnalysis- ',char(easyResultsDir)))
   else
     set(fhImParm,'NumberTitle','off')
-    set(fhImParm,'Name','EASYconsole -Exp. Analysis NOT selected.')
+    set(fhImParm,'Name','EASYconsole - Exp. Analysis NOT selected.')
   end
 
   btnNumber=5;
diff --git a/workflow/templates/easy/NIscanIntensBGpar4GblFnc.m b/workflow/apps/easy/NIscanIntensBGpar4GblFnc.m
similarity index 97%
rename from workflow/templates/easy/NIscanIntensBGpar4GblFnc.m
rename to workflow/apps/easy/NIscanIntensBGpar4GblFnc.m
index 16fa386c..b4263689 100755
--- a/workflow/templates/easy/NIscanIntensBGpar4GblFnc.m
+++ b/workflow/apps/easy/NIscanIntensBGpar4GblFnc.m
@@ -1184,7 +1184,7 @@ function [Tmpsbdg2, scanIntens, F_spots, bmtp, optomizedPos, TmpexpScanIntens2,
       title(strcat('Scan',num2str(selScan),'timePt-',num2str(tPt)));
       clf(hfscanIm,'reset');
       set(hfIm,'NumberTitle','off')
-      set(hfIm,'Name', char(matFile)) % strcat('EASYconsole- ',char(easyResultsDir)))
+      set(hfIm,'Name', char(matFile))
       if Fflg==1
         fullfile(fotosResultsDir,strcat('Scan',num2str(selScan),'_timePt-',num2str(tPt)))  
         hgsave(fullfile(fotosResultsDir,strcat('Scan',num2str(selScan),'_timePt-',num2str(tPt)))); %F 14_0626 
diff --git a/workflow/templates/easy/PTmats/NCFparms.mat b/workflow/apps/easy/PTmats/NCFparms.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/NCFparms.mat
rename to workflow/apps/easy/PTmats/NCFparms.mat
diff --git a/workflow/templates/easy/PTmats/NImParameters.mat b/workflow/apps/easy/PTmats/NImParameters.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/NImParameters.mat
rename to workflow/apps/easy/PTmats/NImParameters.mat
diff --git a/workflow/templates/easy/PTmats/NPTdirectParameters.mat b/workflow/apps/easy/PTmats/NPTdirectParameters.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/NPTdirectParameters.mat
rename to workflow/apps/easy/PTmats/NPTdirectParameters.mat
diff --git a/workflow/templates/easy/PTmats/NPTmapDirect.mat b/workflow/apps/easy/PTmats/NPTmapDirect.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/NPTmapDirect.mat
rename to workflow/apps/easy/PTmats/NPTmapDirect.mat
diff --git a/workflow/templates/easy/PTmats/NPTmapSearch.mat b/workflow/apps/easy/PTmats/NPTmapSearch.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/NPTmapSearch.mat
rename to workflow/apps/easy/PTmats/NPTmapSearch.mat
diff --git a/workflow/templates/easy/PTmats/NPTsearchParameters.mat b/workflow/apps/easy/PTmats/NPTsearchParameters.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/NPTsearchParameters.mat
rename to workflow/apps/easy/PTmats/NPTsearchParameters.mat
diff --git a/workflow/templates/easy/PTmats/Nbdg.mat b/workflow/apps/easy/PTmats/Nbdg.mat
similarity index 100%
rename from workflow/templates/easy/PTmats/Nbdg.mat
rename to workflow/apps/easy/PTmats/Nbdg.mat
diff --git a/workflow/templates/easy/datatipp.m b/workflow/apps/easy/datatipp.m
similarity index 100%
rename from workflow/templates/easy/datatipp.m
rename to workflow/apps/easy/datatipp.m
diff --git a/workflow/templates/easy/figs/NPTdirect.fig b/workflow/apps/easy/figs/NPTdirect.fig
similarity index 100%
rename from workflow/templates/easy/figs/NPTdirect.fig
rename to workflow/apps/easy/figs/NPTdirect.fig
diff --git a/workflow/templates/easy/figs/searchNPTIm.fig b/workflow/apps/easy/figs/searchNPTIm.fig
similarity index 100%
rename from workflow/templates/easy/figs/searchNPTIm.fig
rename to workflow/apps/easy/figs/searchNPTIm.fig
diff --git a/workflow/templates/easy/p4loop8c.m b/workflow/apps/easy/p4loop8c.m
similarity index 100%
rename from workflow/templates/easy/p4loop8c.m
rename to workflow/apps/easy/p4loop8c.m
diff --git a/workflow/templates/easy/par4GblFnc8c.m b/workflow/apps/easy/par4GblFnc8c.m
similarity index 100%
rename from workflow/templates/easy/par4GblFnc8c.m
rename to workflow/apps/easy/par4GblFnc8c.m
diff --git a/workflow/templates/easy/par4Gbl_Main8c.m b/workflow/apps/easy/par4Gbl_Main8c.m
similarity index 96%
rename from workflow/templates/easy/par4Gbl_Main8c.m
rename to workflow/apps/easy/par4Gbl_Main8c.m
index ddcf85f9..a3e4d128 100755
--- a/workflow/templates/easy/par4Gbl_Main8c.m
+++ b/workflow/apps/easy/par4Gbl_Main8c.m
@@ -244,4 +244,3 @@ for scanCnt=1:numScans
   fclose(fid);
 end
 
-EASYconsole;
diff --git a/workflow/docs/.~lock.EASY Manual 17_0614.odt# b/workflow/docs/.~lock.EASY Manual 17_0614.odt#
new file mode 100644
index 00000000..66d6c281
--- /dev/null
+++ b/workflow/docs/.~lock.EASY Manual 17_0614.odt#	
@@ -0,0 +1 @@
+,bryan,workstation,24.07.2024 21:41,file:///home/bryan/.config/libreoffice/4;
\ No newline at end of file
diff --git a/workflow/docs/20_0329_SS_Q_HTCP_Analysis_Readme.md b/workflow/docs/20_0329_SS_Q_HTCP_Analysis_Readme.md
new file mode 100644
index 00000000..c2a8e79b
--- /dev/null
+++ b/workflow/docs/20_0329_SS_Q_HTCP_Analysis_Readme.md
@@ -0,0 +1,491 @@
+**Readme for Q-HTCP analysis using current (2/17/20) practices-**
+
+**![][image1]**
+
+**Figure 1\. Flow diagram for Q-HTCP analysis.** Sections describing steps in current Q-HTCP analysis are indicated numerically.  
+
+**\*\*\* WHAT FOLLOWS IS OUT OF DATE- WE ARE CURRENTLY USING A SET OF SHELL SCRIPT THAT CALLS THE Rscripts DESCRIBED BELOW AND PRODUCES THE DIRECTORIES NEEDED TO LOCATE THE INPUT FILES AND TO RECEIVE THE OUTPUTS. SEE GOOGLE DOC “Copy of Multi-experiment Study Analysis System Guide\_jh24\_0718”**
+
+**\*\*\*Highlighted words indicate places in your code/script that you will need to modify the name to match the files, path, or description of your particular experiment\*\*\***
+
+**It may also be possible to implement Rprojects in order to avoid the need to change file paths. The advantage of Rprojects is that the folder containing the files is the top of the directory, thus the same directory structure can be used on different computers to call and write files from the Rscripts. [seansantos18@gmail.com](mailto:seansantos18@gmail.com)**is this description correct? could this work on the server just as well as it works from our laptops?**
+
+[**seansantos18@gmail.com**](mailto:seansantos18@gmail.com)**also wanted to ask if it is necessary to use FileZilla to move files around on server from Mac, whereas it is possible to use keyboard shortcuts (cont-C / cont-V) on PC, because there is not “middle click” on a Mac?**
+
+Prior to performing interaction score analysis, use John Rodgers Matlab software to generate the \!\!results.txt file. Once the \!\!results.txt file is generated view the experiment using John Rodgers’ software; open Matlab 2014 and the most current version of EZView. Run the EZView.mat program and click “Add to path” when prompted. Navigate to /media/data/ExpJobs/YourJobName/Results-date/matResults/date\_reg.mat to view your plates. Look for plate contamination, plate effects (such as much faster/slower growth than normal, or areas with no growth, gradient from the middle – likely did not mix drug well if seeing this), missing image time points, dropped plates, darker images, and curve fits that do not match visual spot growth. When satisfied with the image results and the Matlab analysis, move on to generating the interaction scores.
+
+**1.0 Generate interaction scores for genome wide experiments from a \!\!results.txt**
+
+1) Create folder on the server for the new experiment (On server, for most experiments, I’ve used the path /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**Folder**/)  
+   It is also possible to create new directory using command line “mkdir”  
+   Example:
+
+![][image2] 
+
+1) Copy \!\!results.txt (results sheet from matlab) to folder (add path to \!\!results.txt files)  
+1) Copy lm\_interaction\_drug\_all\_V5\_do\_not\_print\_avg\_Z.R (interaction score Rscript) to folder (this is located in the Q\_HTCP\_Analysis Folder)  
+1) Open the terminal within /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**Folder**/ path or use command line to navigate to your new folder using “cd”  
+   Example: cd /media/data/Santos\_Sean/Q\_HTCP\_Analysis/Folder/![][image3]
+
+1) Run the interaction scores Rscript from the command line using the following:  
+   Rscript lm\_interaction\_drug\_all\_V5\_do\_not\_print\_avg\_Z.R  \!\!results.txt ExperimentName\_Analysis1/  
+   ![][image4]  
+     
+   ***Modified scripts** can be used to exclude certain data types –* an example we performed was using the JS\_19\_1224 P53 data where the \!\!results.txt contained both WT and 2KR data. We wanted to include only the 2KR data and ran the following modified Rscript:![][image5]  
+   In the above example we used a slightly modified version of the Rscript where we select a subset of the data from \!\!results.txt using the modifications as seen below:  
+   ![][image6]  
+   In the added line of the above R script (last line, shown as opened in the gedit text editor), we subset our data.frame using the specifics column and specify to only include rows where “2KR” is in the specifics column.
+
+   The interaction scores Rscript will generate several files as seen below:  
+   ![][image7]  
+     
+   The **ZScores\_Interaction.csv** file is the primary output containing the interaction scores and is used as the input output file for most of the downstream analysis.  
+   ![][image8]  
+     
+     
+     
+     
+     
+1) After interaction scores are generated, **check the Frequency\_delta\_background.pdf file** in the QC folder to identify data that could possibly be contaminated. The default setting will remove any strains where the delta background is 3SD from the mean, but **if there is no or almost no contamination, you may need to set this threshold higher to 5 or above**. Find the following lines in the lm\_interaction\_drug\_all\_V5\_do\_not\_print\_avg\_Z.R and change 3\*sd to 5\*sd or a higher value. Use **Save As** (not just save) to rename the lm\_interaction\_drug\_all\_V5\_do\_not\_print\_avg\_Z.R to something such as lm\_interaction\_drug\_all\_V5\_do\_not\_print\_avg\_Z\_deltabackground5SD.R.  
+     
+   ![][image9]  
+     
+   **1.1 REMc**  
+     
+   **\*\*\* WHAT FOLLOWS IS OUT OF DATE- THE REMc PROCESS IS NOW INCORPORATED IN A JAR FILE AND IN A SHELL SCRIPT THAT ALSO CALLS Rscripts TO ARRANGE DATE. SEE GOOGLE DOC “Copy of Multi-experiment Study Analysis System Guide\_jh24\_0718”**  
+     
+   At this stage, we want to compare multiple experiments to each other to identify phenomic modules of interest. Recursive Expectation Maximization clustering (REMc) can be used to cluster interaction scores and then perform biological enrichment analysis.  
+   \-See doi: [10.1063/1.3455188](https://doi.org/10.1063/1.3455188) for the Chaos paper and Jingyu Guo’s readme.V2 to set up Eclipse.   
+     
+1) **Merge the files**. This can be done using **R or Excel**; **if you want to use R see appendix 3.1 – you will need to use R if the total number of genes between experiments are not the same\!** Using Excel, open the first **ZScores\_Interaction.csv** file of interest. \*\*\***Sort by OrfRep\*\*\***. **Copy** the **OrfRep, Gene, Z\_Shift\_L, Z\_lm\_L, Z\_Shift\_K, Z\_lm\_K** columns into a new excel sheet. Rename the **Z\_Shift\_L, Z\_lm\_L, Z\_Shift\_K, Z\_lm\_K** to have descriptive names preceding the current column names for example **Gem\_Z\_Shift\_L, Gem\_Z\_lm\_L, Gem\_Z\_Shift\_K, Gem\_Z\_lm\_K**. Open the next **Zscores\_Interaction.csv** file of interest, \*\*\***Sort by OrfRep (don’t forget to do this or the ORFs will not match\!)\*\*\***, and repeat the same steps to add additional experiment columns to the new Excel sheet. Give different descriptive names. For example: **Cyt\_Z\_Shift\_L, Cyt\_Z\_lm\_L, Cyt\_Z\_Shift\_K, Cyt\_Z\_lm\_K**. Repeat this process for as many experiments you want to compare in REMc. I generally reorganize the excel sheet at this step so that the order of columns is how I want to output the heatmaps later on (i.e. All K values to the left, all L values to the right, alternate Z\_Shift and the Z\_lm). I also generally save this file as “Date\_Description\_ShiftInt\_WithNAs.csv” (can also save as excel (.xlsx) format – doesn’t really matter yet). Below is an example of how this file looks (using gemcitabine (Gem) and cytarabine (Cyt) as an example.
+
+   ![][image10]
+
+   
+
+   
+
+   
+
+1) **Substitute the NAs**. For REMc to work correctly and to allow proper heatmap display we will substitute the NA values with small, non-zero values. Open the “Date\_Description\_ShiftInt\_WithNAs.csv” file in excel and **select only the “Shift columns”**. Perform a **find and replace** using find “NA” and replace with “0.001”.
+
+   ![][image11]
+
+   Next, replace the NAs in the Z\_lm columns using **find and replace** to find “NA” and replace with “0.0001”. Note the extra significant digit here – the heatmap script cannot draw the dendrogram if all the values for a gene across a row are NAs and will error out. Thus, we want to be able to identify NAs in one set of columns, but also be able to draw the dendrogram by giving some small value that will cluster away from the rest of the data, and so the script only generates “NAs” in rows with the 0.001 value (shift) but will print the Z\_lm value as a white color to indicate NAs in this way. 
+
+   ![][image12]
+
+   After substitution, the example looks like the following:
+
+![][image13]
+
+I generally name this file “Date\_Description\_ShiftInt\_NArem.csv”
+
+1) **Fill empty cells in the gene column by copying over ORF name. *Some ORFs do not have assigned gene names.* **Fill in the ‘missing’ genes** (if there are any – this will depend on the \!\!results.txt file. If there are blanks in the genes column, this can cause issues with REMc and heatmap generation. If it is a genome wide experiment (making sure it is sorted by OrfRep), I will often copy and paste over the Gene column from a previous genome wide experiment with the gene names filled in. However, I also have an R script that can fill in the missing names and updates gene names (and if no name is given it replaces it with the OrfRep). The Rscript is named **14\_0430\_cmd\_MatchGenes.R**. See the following for usage:
+
+   Rscript 14\_0430\_cmd\_MatchGenes.R input\_file.csv SGD\_features.tab output\_file
+
+   Arg 1\) Input\_file.csv  
+   \-This should be your “Date\_Description\_ShiftInt\_NArem.csv” file generated in step 2\. Make sure that OrfRep or ORF is in column 1 and Gene is in column 2\.  
+   Arg 2\) SGD\_features.tab – download this from [https://downloads.yeastgenome.org/curation/chromosomal\_feature/](https://downloads.yeastgenome.org/curation/chromosomal\_feature/)   
+   Arg 3\) output\_file  
+   \-The name you want to give the file. I generally call this file “Date\_Description\_ShiftInt.csv”
+
+![][image14]
+
+We will also need to remove all unusual characters in the gene column after adding the gene names (as above, either by copy/paste or by 14\_0430\_cmd\_MatchGenes.R). Open the newly created file (“Date\_Description\_ShiftInt.csv”) in excel and use the find function to **look for the following characters** – comma (**,**) or asterisk (**‘**) and **replace the gene name with the OrfRep** in all instances. Also search for **YKL134C** – in the corresponding gene column, excel will auto change this to a date (October 1). Change the gene name to YKL134C to prevent this conversion at any step.
+
+1) **Select only the L and K z-scores above |2| for input into REMc** (if you want to cluster all the data you can skip this step, but in general selecting only the values above 2SD tends to clean up the clustering). I generally do this as an “ad-hoc” analysis in R and will give example code below. Open RStudio and create a new R Script. You can copy and paste the following into R and will only need to modify bolded parts (see below):
+
+   X \<- read.csv(file="**filePath/Date\_Description\_ShiftInt.csv**",stringsAsFactors \= FALSE)  
+     
+   X \<- X\[abs(X$**DescName1**\_Z\_lm\_K) \>= 2 | abs(X$**DescName1**\_Z\_lm\_L) \>= 2 | abs(X$**DescName2**\_Z\_lm\_K) \>= 2 | abs(X$**DescName2**\_Z\_lm\_L) \>= 2,\]  
+     
+   write.csv(X,file \= "**filepath/Date\_Description\_ShiftInt\_Above2SD.csv**",row.names \= FALSE)  
+   ![][image15]
+
+   \-change “**filePath/Date\_Description\_ShiftInt.csv**” to the file path to your file and then the name of your file. Use tab within the quotation marks in RStudio to help locate files.  
+   \-Change “**DescName1**” and “**DescName2**” to whatever descriptive name you gave your headers in Date\_Description\_ShiftInt.csv (i.e. could be “Gem” and “Cyt”)  
+   \-replace “**filepath/Date\_Description\_ShiftInt\_Above2SD.csv**” with a path to the file you want to create.   
+   \-Save and run the above R script with a descriptive name such as “Date\_Get2SD\_for\_ExperimentDescription.R” in case you need to go back and see what you did.  
+     
+1) **Generate files for REMc** and for later adding the shift values back into the heatmaps (we don’t cluster the shift values but are interested in using them at the heatmap stage to identify genes where deletion results in a large initial shift indicating “sick” strains). \-Open the “Date\_Description\_ShiftInt\_Above2SD.csv” created in step 4 (or “Date\_Description\_ShiftInt.csv” generated in step 3 if step 4 was skipped and you want to use all genes regardless of z-score for clustering) in Excel.  
+   i) **Remove** all of the **columns with shift values** and save the file as “**Date\_Description\_REMcReady.csv**”. This file is used as input for REMc.  
+   ii) Reopen “Date\_Description\_ShiftInt\_Above2SD.csv” and **remove** all the **columns with Z\_lm scores**. Save this file as “Date\_Description\_Shift.csv”. This file will later be used to add the shift values back when generating heatmaps associated with REMc generated clusters.
+
+1) **Perform REMc**. Copy the “**Date\_Description\_REMcReady.csv**” file generated in step 5 to your eclipse-workspace/REMc/ directory. **Open eclipse** by double clicking on the eclipse icon in the eclipse folder in your home folder. Once in eclipse, **select “Run” and then** “**Run configurations**” from the top menu. 
+
+![][image16]  
+Next, select the Arguments tab and look to see that you have the following: 
+
+Program arguments:
+
+Date\_Description\_REMcReady.csv 
+
+GeneByGOAttributeMatrix\_nofiltering-2009Dec07.tab
+
+ORFs\_w\_DAmP\_list.txt
+
+1
+
+true
+
+VM arguments: 
+
+\-Xms8000m \-Xmx8000m
+
+![][image17]
+
+Once set, you will only need to alter the first line in program arguments to the name of your **Date\_Description\_REMcReady.csv** file. Select **apply** and then **run** in the bottom right corner to perform REMc on the selected file. Look to see if any errors are generated after hitting run (common errors here are due to unexpected characters in the ORF or Gene columns, or non-numeric values in the other columns (such as “NA”). If REMc is running, in the eclipse-workspace/REMc/ directory you should see a file called **Date\_Description\_REMcReady.csv-WholeTree.csv**. This file will be updated every few minutes as new clusters are generated. After completion of REMc, you should have **Date\_Description\_REMcReady.csv-WholeTree.csv**, **Date\_Description\_REMcReady.csv-finalTable.csv**, **Date\_Description\_REMcReady.csv-summary.csv**, **Date\_Description\_REMcReady.csv.arff**. Copy these files to a new directory with a descriptive name to perform REMc (such as **Date\_Description\_Clustering/**). 
+
+**1.1.1 GTF (Gene Ontology Term Finder)**
+
+GTF will look for enriched gene ontology terms in the REMc clusters. Several files are required to run GTF and they must be copied to the working folder (I usually create a new folder to contain all of the REMc/GTF files) to run GTF.
+
+1\) **Make a new folder, e.g., ‘Clustering’, in the project folder and** **Copy REMc and GTF files to the new folder**. (Most of Sean’s Clustering results are on Data2/Santos\_Sean/Documents/Hartman\_Lab/ACS\_project/). Once REMc is finished, which is described in the previous section, copy the following files to your working folder **Date\_ResultsDescription\_REMcReady.csv-finalTable.csv**, **Date\_ResultsDescription\_REMcReady.csv-WholeTree.csv, Date\_ResultsDescription\_REMcReady.csv-summary.csv**. Next, copy to your working folder the files located in the following directory on the server: **/media/data/Santos\_Sean/GTF\_Files/**
+
+![][image18]
+
+If the copy/paste function doesn’t work for you on the server from Xquartz (we’ve had issues with this on Mac OS), then use FileZilla to copy the files to your computer (files in **/media/data/Santos\_Sean/GTF\_Files/**) and then you can copy the files from wherever you save them on your local computer to your working folder on the server. It’s good practice to make a backup of the files off of the server, but one can skip this step by copying them to the server desktop (which exists on a different drive) and then copy back again to the desired folder (dragging between folders on the same drive moves rather than copies the files).
+
+In your working directory, create folders named “Process”, “Function”, and “Component” and we will perform GTF for each of these ontologies in these folders. Copy the GTF files into these folders. (The GTF files can be removed from these folders after GTF is complete to save space). Copy **DconJG.py and AddShiftVals.R** into the parent folder as well (needs to be in same directory as the **.csv-finalTable.csv** file. In the example of P53\_NoDamps (involving HLD, HLEG, WT and 2KR), there were 5 different comparisons, with component, function and process GTF for each. Also note, ‘Pairwise\_Comparisons’ and ‘Multiple\_Comparisons’ folders are GTA only.
+
+2\) **Run the DconJG.py script**. GTF requires files to have a specific format and to generate text files for each cluster in this format we can apply the DconJG.py script. To run this script, open the command line in your working folder and apply the script in the following format:
+
+**python DconJG.py Date\_Results\_REMcReady.csv-finalTable.csv cluster\_origin\_column\_num output\_path\_name**
+
+\-the cluster\_origin\_column\_num tells the script where the header/column containing “cluster\_origin” is located in the \-finalTable.csv file. (i.e. – is it in the 8th column, 10th column, etc.?)
+
+![][image19]
+
+\-I usually put a “./” as the argument for “output\_path\_name”, which makes a folder in my working directory with the same name as my input \-finalTable.csv file. Copy the resulting folder containing the .txt files for each cluster into the “Process”, “Function” and “Component” directories.
+
+3\) **Perform GTF**. In the process folder in your working directory, run the “**Process\_example\_v4.sh**” script:
+
+**./Process\_example\_v4.sh ORFs\_w\_DAmP\_list.txt Date\_Results\_REMcReady/\*.txt**
+
+\-If necessary, substitute “ORFs\_w\_DAmP\_list.txt” with the proper background file containing a list of ORFs to include as the background for GTF. For example, if DAmPs were excluded use the 17\_0503\_ORF\_list\_without\_DAmPs.txt file.
+
+![][image20]
+
+In the Function and Component folder, run the “**Component\_example\_v2.sh**” and “**Function\_example\_v2.sh**” scripts in their respective folders using the same arguments as the Process\_example\_v2.sh.  \*\*\* if you do not run these scripts in separate folders, you will overwrite the results from one ontology with another\*\*\*
+
+In your **Date\_DescriptionOfResults\_REMcReady/** folder, new .terms.txt and .tsv text files should be generated ) and GTF will run through all .txt files for clusters (can take 10 mins to an hour depending on how many total clusters). The example below shows the files created after running GTF.
+
+![][image21]
+
+4\) **Concatenate the GTF results.** Use the Concatenate\_GTF\_results.py file to concatenate the GTF results for each ontology by running this script in the folder for each respective ontology. The script can be used in the following way:
+
+python Concatenate\_GTF\_results.py Date\_DescriptionOfResults\_REMcReady/ Date\_GTF\_Results\_**Ontology**\_DescriptionoOfResults.txt
+
+![][image22]
+
+\-Make sure to change “Ontology” in the above line to either “Process”, “Function”, or “Component”.
+
+Lastly, I prefer to compile the concatenated results sheets into one excel document. To do this, I will copy the files to my computer using FileZilla and then copy and paste the tables to separate worksheets in the same Excel document and save this file as Date\_GTF\_Results \_DescriptionOfResults.xlsx
+
+**1.1.2 Heatmap generation for REMc**. Automatic generation of heatmaps from REMc clusters can be performed using the Rscript “**18\_0205\_heatmaps\_zscores\_2SD\_color\_NARem\_Z\_lm.R**”. This file can be found in **/media/data/Santos\_Sean/Rscripts/REMc\_Heatmaps/**
+
+Before we make heatmaps, we prefer to add the shift values back with the interaction scores in Date\_Description\_REMcReady.csv-finalTable.csv.
+
+1\) **Add back the shift values**. Use “**AddShiftVals.R**” to add the shift values back to the Date\_Description\_REMcReady.csv-finalTable.csv. Use the script in the following format:
+
+Rscript AddShiftVals.R Date\_Description\_REMcReady.csv-finalTable.csv Date\_Description\_Shift.csv Date\_Description\_REMcReady.csv-finalTable**WithShift**.csv . Note: ‘Date\_Description\_Shift.csv’ was saved when the shift values were removed just prior to REMc (see 1.1\_5)). 
+
+This will generate a file with both the interaction scores and the shift values in the finalTable.csv format. However, we will need to **reorganize** the Date\_Description\_REMcReady.csv-finalTable**WithShift**.csv so that each shift values is correctly ordered to the left of its associated Z\_lm interaction score. Open the Date\_Description\_REMcReady.csv-finalTable**WithShift**.csv file in excel and manually reorder the shift values to their correct location. 
+
+2\) **Run the heatmap script**. Use **18\_0205\_heatmaps\_zscores\_2SD\_color\_NARem\_Z\_lm.R** to generate the heatmaps. Use this script as described below:
+
+Rscript 18\_0205\_heatmaps\_zscores\_2SD\_color\_NARem\_Z\_lm.R Date\_Description\_REMcReady.csv-finalTable**WithShift**.csv Heatmaps/
+
+\-this will create a folder called “Heatmaps” in your directory with PDFs for all REMc clusters. 
+
+3\) **Concatenate the heatmaps into one PDF**. Use pdf tool kit to concatenate the heatmaps by running the following line in the command line in your working folder.
+
+pdftk Heatmaps/\*.pdf output Date\_Description\_Heatmaps.pdf
+
+**1.2** **GTA Analysis**. Gene Ontology (GO) term averaging (GTA) is performed first on a single experiment (1.2.1) and later can be compared pairwise to other GTA experiments (1.2.2) by generating interactive plots. GTA assigns GO terms an average Z score based on the interaction z-scores (L- or K-based) for all genes in that GO term, and then assigns significance to GO terms with a Z-score above |2| after subtracting the standard deviation. In general, we have observed that GTA tends to identify smaller GO terms that may not have been identified by REMc/GTF.
+
+**1.2.1 Generate GTA results.**
+
+Open terminal in or navigate in the terminal to /media/data/Santos\_Sean/Q\_HTCP\_Analysis/. In this folder we will use the ScoreAllGOTerms\_From\_Z\_lm\_V2.R scriptgen to generate GTA scores. Run this script on your experiment using the following arguments/files:
+
+Rscript ScoreAllGOTerms\_From\_Z\_lm\_V2.R Exp\_Name/Analysis1/ZScores\_Interaction.csv go\_terms.tab gene\_association.sgd Exp\_Name/GTA\_Results/
+
+\-Several files will be generated in the specified directory, but the file with all of the GTA results is named “**Average\_GOTerms\_All.csv**” and will be used in the next step of pairwise analysis of experiments.
+
+![][image23]
+
+**1.2.2 Pairwise GTA analysis.** Comparing GTA between two experiments will create the interactive plots and associated tables for GTA identified GO terms. This analysis can be performed in the /media/data/Santos\_Sean/Q\_HTCP\_Analysis/ directory using the following scripts: **Compare\_GTF\_Averages\_BetweenScreens\_lm\_v2.R** for L values and **Compare\_GTF\_Averages\_BetweenScreens\_lm\_Kvals\_v2.R** for K values.
+
+The two scripts take the same arguments (you only need to specify which script is being used for L or K after writing Rscript in the command line) and can be run using the following:
+
+Rscript Compare\_GTF\_Averages\_BetweenScreens\_lm\_v2.R Exp1/GTA\_Results/ Average\_GOTerms\_All.csv Exp1\_Name Exp2/GTA\_Results/Average\_GOTerms\_All.csv Exp2\_Name Pairwise\_Comparisons/Exp1\_vs\_Exp2/GTA\_L/
+
+\-In the Pairwise\_Comparisons directory, you will need to create the folder for your comparison first because the Rscript will only allow you to make one new directory time
+
+**1.3 Term Specific Heatmaps**
+
+Open terminal to or navigate in the terminal to /media/data/Santos\_Sean/Q\_HTCP\_Analysis/ and the term specific heatmaps Rscripts are in this directory.
+
+\-Depending on the number of experiments you want to compare, select one of the following scripts: **GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_V2.R** can be used to compare two experiments, **GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_3terms\_V2.R** for 3 experiments, **GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_4terms\_V2.R** for 4, **GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_5terms\_V2.R** for 5\.
+
+![][image24]
+
+Follow the directions for the Rscript appropriate for the number of experiments you are comparing by opening the Rscript in either Rstudio or gedit and looking at what arguments to use. The number of arguments will be greater if a greater number of experiments are being compared. For the GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_V2.R Rscript it can be used in the command line using the following:
+
+Rscript GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_V2.R Exp1\_ZScores\_Interaction.csv Exp1\_name Exp2\_ZScores\_Interaction.csv Exp2\_Name gene\_ontology\_edit.obo go\_terms.tab All\_SGD\_GOTerms\_for\_QHTCPtk.csv Pairwise\_Comparisons/Exp1\_vs\_Exp2/TermSpecificHeatmaps/
+
+\-The All\_SGD\_GOTerms\_for\_QHTCPtk.csv file can be substituted for another file with a list of GO\_IDs if you only want to run it on certain GO\_terms – I’ve created a few examples in the following path: /media/data/Santos\_Sean/Q\_HTCP\_Analysis/Pairwise\_Comparisons/Query\_Term\_Lists/ 
+
+\-For greater than 2 experiments, there will be more arguments to load in the Exp\#\_ZScores\_Interaction.csv and the Exp\#\_Name.
+
+**3\. Other Rscripts**
+
+**3.1 Pairwise Venn Diagrams and CPP Correlation plots to compare two experiments.** I generally perform this analysis right after performing GTA and creating a folder for the two experiments I am comparing in the **Pairwise\_Comparisons** folder. Use the Rscript at the following file path: /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**Compare\_Pairwise\_Overlap\_VennDiagrams\_confined\_to\_matches\_V2.R**
+
+Usage (shown as run from /media/data/Santos\_Sean/Q\_HTCP\_Analysis/): 
+
+Rscript Compare\_Pairwise\_Overlap\_VennDiagrams\_confined\_to\_matches\_V2.R Exp1/Exp1\_Analysis1/Exp1\_InteractionScores.csv Exp1\_Name Exp2/Exp2\_Analysis1/Exp2\_InteractionScores.csv Exp2\_Name Pairwise\_Comparisons/Exp1\_vs\_Exp2/VennDiagrams\_and\_Correlation/
+
+This script will output Venn Diagrams for overlap between the two experiments when comparing enhancers and suppressors (defined as Z \> |2|). It will generate lists for the intersecting genes, and genes that are experiment-specific deletion enhancers or suppressors as .csv tables. It will also perform a CPP comparison by plotting the L, K, R, and AUC values across experiments and print a correlation coefficient (R2) for a linear regression fit. It will also create graph plotting the ranked CPP scores against each other. There will be four output folders but using the updated model we only want to look at the “**lm\_both**” results. **Ignore** the “Avg\_Zscore\_Both (alex’s method)” and the “lm\_exp1\_only” and “lm\_exp2\_only” (these compare alex’s method and the updated linear model method). I can remove these extra comparisons if too confusing.
+
+**3.2 CPP analysis for YKO/YKD/RF for one experiment (all CPPs by all CPPs).** Use the Rscript at the following file path: /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**CPP\_Comaprison\_with\_DAmPs\_and\_RF.R**
+
+Usage (shown as run from /media/data/Santos\_Sean/Q\_HTCP\_Analysis/): 
+
+Rscript CPP\_Comaprison\_with\_DAmPs\_and\_RF.R ExpName/ExpName\_Analysis1/ExpName\_InteractionScores.csv  ExpName/ExpName\_Analysis1/ExpName\_**RF\_InteractionScores**.csv 16\_0531\_DAmPs\_Only.csv ExpName/ExpName\_Analysis1/CPP\_Compare/
+
+\-Three PDFs will be created after this analysis: 1\) CPPs only for the YKO. 2\) CPPs from YKO and YKD as different colors. 3\) YKO, YKD and RF as different colors.
+
+\-After generating these files, I check to make sure that the RF interaction scores generally fall into the range of \+2 to \-2. I also look to see if the DAmPs have a different distribution than the YKO.
+
+**3.3 Heatmaps with homologs.** Use the Rscript at the following file path: /media/data/Santos\_Sean/GTF\_files/**20\_0328\_heatmaps\_Z\_lm\_wDAmPs\_andHomology.R**
+
+Uses the following arguments:
+
+1) EXP\_REMcReady.csv-finalTableWithShift.csv  
+1) Output folder  
+1) 17\_0503\_DAmPs\_Only.txt (see path below)  
+1) Yeast\_Human\_Homology\_Mapping\_biomaRt\_18\_0920.csv (see path below)
+
+Usage: Rscript 20\_0328\_heatmaps\_Z\_lm\_wDAmPs\_andHomology.R EXP\_REMcReady.csv-finalTableWithShift.csv Heatmaps\_Homologs/ /media/data/Santos\_Sean/GTF\_files/17\_0503\_DAmPs\_Only.txt /media/data/Santos\_Sean/GTF\_files/Yeast\_Human\_Homology\_Mapping\_biomaRt\_18\_0920.csv  
+
+\-The script will generate a folder with heatmaps including homology info for the genes in the finalTable file and two .csv files, one with all the yeast genes from the original finalTable (even without homologs) and one list only with the homologs.
+
+**4.0 Appendix.**
+
+**4.1** **Using R to merge files** (see REMc 1.1 step 1). R can be used to merge two tables using the join function from the plyr package. If you have not installed this package (only need to do this once), open R studio and type install.packages(“plyr”) into the console and follow the instructions to install the package. In R studio modify the following lines by changing the highlighted portion to the path to your files
+
+\#open required library for the join function
+
+library(plyr)
+
+\#read in the files for your experiment
+
+X1 \<- read.csv(file="FilePath/Exp1\_ZScores\_Interaction.csv",stringsAsFactors \= FALSE)
+
+X2 \<- read.csv(file=" FilePath/Exp2\_ZScores\_Interaction.csv",stringsAsFactors \= FALSE)
+
+\#join the two files, **list the larger file first** – in this example X2 has the larger number of genes.
+
+\#if X1 has a larger number of genes, switch the order of X1 and X2
+
+X \<- join(X2,X1,by="OrfRep")
+
+\#write new file
+
+write.csv(X,file \= " FilePath/DescriptiveName\_withNAs.csv",row.names=F)
+
+\-If you need to join more than two tables together you will have to write more than one join. You will first need to join two of the tables, create an object, and then join that object with the next file, and so on…
+
+See below for an example of multiple files
+
+\#open required library for the join function
+
+library(plyr)
+
+\#read in the files for your experiment
+
+X1 \<- read.csv(file="FilePath/Exp1\_ZScores\_Interaction.csv",stringsAsFactors \= FALSE)
+
+X2 \<- read.csv(file=" FilePath/Exp2\_ZScores\_Interaction.csv",stringsAsFactors \= FALSE)
+
+X3 \<- read.csv(file=" FilePath/Exp3\_ZScores\_Interaction.csv",stringsAsFactors \= FALSE)
+
+X4 \<- read.csv(file=" FilePath/Exp4\_ZScores\_Interaction.csv",stringsAsFactors \= FALSE)
+
+\#join the two files, list the larger file first – in this example X2 has the largest number of genes.
+
+\#if X1 has a larger number of genes, switch the order of X1 and X2
+
+X \<- join(X2,X1,by="OrfRep")
+
+X \<- join(X,X3,by=”OrfRep”)
+
+X \<- join(X,X4,by=”OrfRep”)
+
+\#write new file
+
+write.csv(X,file \= " FilePath/DescriptiveName\_withNAs.csv",row.names=F)
+
+**5.2 Removing DAmPs**
+
+Use the R script at the following path: /media/data/Santos\_Sean/Q\_HTCP\_Analysis/Exclude\_DAmPs.R
+
+Rscript Exclude\_DAmPs.R Input\_File.csv 17\_0503\_DAmPs\_Only.txt output\_file.csv
+
+Arg 1 – Use any file with OrfRep column that you want to remove the DAmPs from, for example: Zscores\_Interaction.csv or REMcReady.csv, \-finalTable.csv files
+
+Arg 2 \- /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**17\_0503\_DAmPs\_Only.txt**
+
+Arg 3 – output file name; make sure to have the .csv extension.
+
+**\-Alternatively, create a new script using the following script by Copy and pasting the following lines into R and modify the highlighted sections to match the file you want to** 
+
+X \<- read.csv(file="path/FileToRemoveDAmPsFrom.csv",stringsAsFactors \= FALSE)
+
+Damps \<- read.delim(filepath/**17\_0503\_DAmPs\_Only.txt**",header=F)
+
+\#create a column in X1 called ORF so we can remove OrfRep numbers and find all the DAmPs
+
+X$ORF \<- X$OrfRep
+
+\#remove \_1-4 from newly created ORF column
+
+X$ORF \<- gsub("\_1","",x=X$ORF)
+
+X$ORF \<- gsub("\_2","",x=X$ORF)
+
+X$ORF \<- gsub("\_3","",x=X$ORF)
+
+X$ORF \<- gsub("\_4","",x=X$ORF)
+
+X \<- X\[\!(X$ORF %in% Damps$V1),\]
+
+write.csv(X,file \= "path/output\_file\_noDAmPs.csv",row.names \= FALSE)
+
+\*the **17\_0503\_DAmPs\_Only.txt** file is in the following path on the server:
+
+/media/data/Santos\_Sean/GTF\_Analysis/**17\_0503\_DAmPs\_Only.txt**
+
+\-The above script could also be used to remove any set of genes from another, but you would substitute the 17\_0503\_DAmPs\_Only.txt with a set of OrfReps saved as a tab delimited file with one ORF per line and no header.
+
+**5.3 Adjust ZScores for YKO only and remove DAmPs**
+
+\-We discussed a script that would remove the DAmPs and also adjust the ZScores for CPPs to only consider the YKO strains in the z-score calculation. 
+
+Use the file at the following path: /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**Adjust\_YKO\_Zscores\_RemoveDAmPs.R**
+
+Usage:
+
+Rscript Adjust\_YKO\_Zscores\_RemoveDAmPs.R ZScores\_Interaction.csv 17\_0503\_DAmPs\_Only.txt AdjustedZScores\_noDAmPs/
+
+Arg 1 – Use the Zscores\_Interaction.csv file where you want to adjust the YKO ZScores and remove the damps
+
+Arg 2 \- /media/data/Santos\_Sean/Q\_HTCP\_Analysis/**17\_0503\_DAmPs\_Only.txt**
+
+Arg3 – a file path to put the files that will be created into (will create a new ZScores\_Interaction.csv with the adjusted scores in the Z\_lm\_ columns, a scatterplot of the initial Z scores vs adjusted, and new rank plots for the adjusted scores).
+
+**5.4 Files that are updated from databases.**
+
+gene\_ontology\_edit.obo \- [www.geneontology.org/ontology/gene\_ontology\_edit.obo](http://www.geneontology.org/ontology/gene\_ontology\_edit.obo) 
+
+SGD\_features.tab \- [https://downloads.yeastgenome.org/curation/chromosomal\_feature/](https://downloads.yeastgenome.org/curation/chromosomal\_feature/)
+
+go\_terms.tab \- [https://downloads.yeastgenome.org/curation/literature/](https://downloads.yeastgenome.org/curation/literature/) 
+
+\*\*\*\*\*\*\*\*\*\*Adding information about how JH updated files in summer of ‘23:
+
+Updating Q-HTCP Source Files:  
+   
+**gene\_ontology\_edit.obo**  
+             
+Direct link to the latest file:   
+[https://purl.obolibrary.org/obo/go.obo](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpurl.obolibrary.org%2Fobo%2Fgo.obo\&data=05%7C01%7Cjhartman%40uab.edu%7C0cf8e0ce5ae74943d3bd08dbc1388a83%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638316220828714336%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=4LRsWT5N44VOOrAX9aby7qWa5ece0NDh6BlhW0r%2FAdA%3D\&reserved=0)  
+   
+^^copy this into a new text file and give the same name.  
+   
+More info about the file, where it comes from, and the Gene Ontology consortium:   
+[https://geneontology.org/docs/download-ontology/](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgeneontology.org%2Fdocs%2Fdownload-ontology%2F\&data=05%7C01%7Cjhartman%40uab.edu%7C0cf8e0ce5ae74943d3bd08dbc1388a83%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638316220828714336%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=s%2FDjYbaA%2FkK%2FwIKsfGhKnS%2BconR11mkBrnxnlX9QMrw%3D\&reserved=0)  
+   
+You can also use SGD's GO Term Finder, which always has the latest ontology and annotations:   
+[https://www.yeastgenome.org/goTermFinder](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.yeastgenome.org%2FgoTermFinder\&data=05%7C01%7Cjhartman%40uab.edu%7C0cf8e0ce5ae74943d3bd08dbc1388a83%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638316220828714336%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=EiFqHvWQI49caPBayi44CbfVfXcqqXjTeN9TMY8%2Faz8%3D\&reserved=0)  
+   
+   
+**SGD\_features.tab**  
+   
+Best to use YeastMine – see notes and files from work/yeast strains/ **’23\_0914\_NewMPFile\_Construction.xlsx’**  
+Need to use the new, updated KO ORF list (made compatible with the final SGD edition of the genome) to get the new gene names, and just replace them in an existing file to update.  
+   
+   
+**Go\_terms.tab**  
+   
+The go\_terms.tab file can be generated using YeastMine with a single click.  
+   
+From the ‘Retrieve GO Terms’ template ([https://yeastmine.yeastgenome.org/yeastmine/template.do?name=GO\_Terms\_Tab\&scope=all](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fyeastmine.yeastgenome.org%2Fyeastmine%2Ftemplate.do%3Fname%3DGO\_Terms\_Tab%26scope%3Dall\&data=05%7C01%7Cjhartman%40uab.edu%7C2eec1f6ea1c14c0971a408dbc13bc3c9%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638316235117030364%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=1KnL5BeJIw3LHr%2F4vy%2FoM1sh%2FBMV0tXQBYtHpaIqHCQ%3D\&reserved=0)), click the green ’Show Results’ button, voila.  You can download the file via the ‘Export’ button on the results page.
+
+The updated Go\_terms.tab file was problematic \- it was different from the original in the style of (col 1\) GO\_ID column entries (e.g., ‘GO:0000001’ instead of ‘1’, and (col 3\) ‘biological\_process’ instead of ‘P’. It also had 122 fewer rows/entries (42887 instead of 4409\).   
+   
+The above columns can be fixed by using “text to columns” with ‘:’ as the delimiter, and then doing find/replace for P, F, and C.  
+File updated 2024\_0125 (42,442 rows). Original had 44,009 and last update 42,887… so numbers are getting smaller. One can find updates at: Gene Ontology FAQ: [https://geneontology.org/docs/faq/\#ontology](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgeneontology.org%2Fdocs%2Ffaq%2F%23ontology\&data=05%7C02%7Cjhartman%40uab.edu%7C4f087305933b4fa99c0a08dc1df646e3%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638418190840735342%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=bkb%2FAv350Z12qddJZeFLDFgxHYxM%2FWHazBOVJ%2BPeO4Q%3D\&reserved=0)
+
+**gene\_association.sgd**  
+   
+The gene\_association.sgd file (‘gaf’) is still served from the Downloads site and can also be accessed even more easily via SGD search.  
+   
+Downloads site:   
+[http://sgd-archive.yeastgenome.org/curation/literature/](https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fsgd-archive.yeastgenome.org%2Fcuration%2Fliterature%2F\&data=05%7C01%7Cjhartman%40uab.edu%7C4985afdc22f24a1ff21d08dbc1422ebe%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638316262252331525%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=Fc4mkGqT2a3UL8gNeObm7huuQR%2FzH1KTWd%2FZjpStlz8%3D\&reserved=0)  
+   
+From SGD Search (search for ‘gaf’, then click category ‘Downloads’):   
+[https://www.yeastgenome.org/search?q=gaf\&category=download\&status=Active](https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.yeastgenome.org%2Fsearch%3Fq%3Dgaf%26category%3Ddownload%26status%3DActive\&data=05%7C01%7Cjhartman%40uab.edu%7C4985afdc22f24a1ff21d08dbc1422ebe%7Cd8999fe476af40b3b4351d8977abc08c%7C1%7C0%7C638316262252331525%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C\&sdata=JTDNv83D31LvU25UynBuVor7V6X4z28hpU6Sml3HIbc%3D\&reserved=0)   
+   
+\*\*\*\*\*\*\*\*\*\*\*\*
+
+[image1]: 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>
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+[image2]: 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>
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+[image3]: 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>
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+[image4]: 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>
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+[image5]: 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>
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+[image6]: 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>
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+[image7]: 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>
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+[image8]: 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>
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+[image9]: 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>
+
+[image10]: 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>
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+[image11]: 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>
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+[image12]: 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>
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+[image13]: 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>
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+[image14]: 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>
+
+[image15]: 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>
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+[image16]: 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>
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+[image17]: 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>
+
+[image18]: 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>
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+[image19]: 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>
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+[image20]: 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>
+
+[image21]: 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>
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+[image22]: 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>
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+[image23]: 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>
+
+[image24]: 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>
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+**QHTCP \- Hartman Lab User’s Guide**                                                   
+
+**Overview and Introduction to Directory Structure**
+
+There should be at least 4 subdirectories to organize Q-HTCP data and analysis. The parent directory is simply called ‘Q-HTCP’ and the 4 are subdirectories described below (**Fig. 1**):
+
+1. ‘ExpJobs’- This directory contains raw image data and image analysis results for the entire collection of Q-HTCP experiments. We recommend each subdirectory within ‘ExpJobs” should represent a single Q-HTCP experiment and be named using the following convention (AB yyyy\_mmdd\_PerturbatationsOfInterest):  experimenter initials (‘AB ‘), date (‘yyyy\_mmdd\_’), and brief description (‘drugs\_medias’). Each subdirectory contains the Raw Image Folders for that experiment (a series of N folders with successive integer labels 1 to N, each folder containing the time series of images for a single cell array). It also contains a user-supplied subfolder, which must be named ‘’MasterPlateFiles” and must contain two excel files, one named ‘**DrugMedia\_*experimentdescription*’ and the other named  ‘**MasterPlate\_*experimentdescription*’. The bolded part of the file name including the underscore is required. The italicized part is optional description. Generally the ‘DrugMedia\_’ file merits description. If the standard MasterPlate\_Template file is being used, it’s not needed to customize then name. On the other hand if the template is modified, it is recommended to rename it and describe accordingly \- a useful convention is to use the same name for the MP files as given to the experiment (i.e, the parent ExpJobs subdirectory described above) after the underscores. The ‘MasterPlate\_’  file contain associated cell array information (culture IDs for all of the cell arrays in the experiment) while the ‘DrugMedia\_’ file contains information about the media that the cell array is printed to. Together they encapsulate and define the experimental design. The QHTCPImageFolders and ‘MasterPlateFiles’ folder are the inputs for image analysis with EASY software. As further described below, EASY will automatically generate a ‘Results’ directory (within the ExpJobs/‘ExperimentJob’ folder) with a name that consists of a *system-generated timestamp* and an optional short description provided by the user (**Fig.2**). The ‘Results’ directory is created and entered, using the “File \>\> New Experiment” dropdown in EASY. Multiple ‘Results’ files may be created (and uniquely named) within an ‘ExperimentJob’ folder.  
+     
+1. ‘EASY’- This directory contains the GUI-enabled MATLAB software to accomplish image analysis and growth curve fitting. EASY analyzes Q-HTCP image data within an ‘ExperimentJob’’ folder (described above; each cell array has its own folder containing its entire time series of images). EASY analysis produces image quantification data and growth curve fitting results for each cell array; these results are subsequently assembled into a single file and labeled, using information contained in the ‘MasterPlate\_’ and ‘DrugMedia\_’ files in the ‘MasterPlateFiles’ subdirectory. The final files (named ‘\!\!ResultsStd\_.txt’ or ‘\!\!ResultsELr\_.txt’) are produced in a subdirectory that EASY creates within the ‘ExperimentJob’ folder, named ‘/Results*TimeStampDesc*/PrintResults’ (**Fig. 2**). The /EASY directory is simply where the latest EASY version resides (additional versions in development or legacy versions may also be stored there). Note: The raw data inputs and result outputs for EASY are kept in the ‘ExpJobs’ directory. EASY also outputs a ‘.mat’ file that is stored in the ‘matResults’ folder and is named with the TimeStamp and user-provided name appended to the ‘Results’ folder name when ‘New Experiment’ is executed from the ‘File’ Dropdown menu in the EASY console.  
+1. ‘EZview’- This directory contains the GUI-enabled MATLAB software to conveniently and efficiently mine the raw cell array image data for a Q-HTCP experiment. It takes the Results.m file (created by EASY software) as an input and permits the user to navigate through the raw image data and growth curve results for the experiment. The /EZview provides a place for storing the the latest EZview version (as well as other EZview versions).  EZview provides a GUI for examining the EASY results as provided in the …/matResults/… .mat file.  
+1. ‘StudiesQHTCP’ \- A software composite (MATLAB, JAVA, R, Python, Perl, Shell) that takes growth curve results (created by EASY software) as an input and successively generates interaction Z-score results, which are used for graphing gene interactions, Clustering, Gene Ontology analysis, and other ways of interpreting and visualizing the experimental quality and outcomes. {The /StudiesQHTCP folder contains the ordered command line scripts that call sets of other scripts to perform data selection and adaptation from the extracted text results spreadsheet found in the /ExpJobs/*experiment name*/Results…/PrintResults/ folder. In particular the ‘*user customize interactionCode4experiment*.R’ file. It also contains a multitude of R generated plots based on the selected data and possible adaptation.  All clustering and Gene ontology analysis are derived from the ‘ZScores\_Interaction.csv’ file found in the/ZScores subdirectory.}   
+1. ‘Master Plates’ \- This optional folder is a convenient place to store copies of the ‘MasterPlate\_’ and a ‘DrugMedia\_’ file templates, along with previously used files that may have been modified and could be reused or further modified to enable future analyses. These two file types are required in the ‘MasterPlateFiles’ folder, which catalogs experimental information specific to individual Jobs in the ExpJobs folder, as described further below.   
+   
+
+**ExpJobs**
+
+1. The ExpJobs folder contains subdirectories, named accordingly for each experiment. Inside the respective experiment directory, folders containing the time series for each cell array that is part of the experiment are named, numerically, ‘1’, ‘2’, …. There should be one folder for each cell array (these are generated at the image collection stage). The images are provided for this Q-HTCP study, which consists of two experiments. In addition to the image folders, each experiment will contain a subdirectory, named ‘MasterPlateFiles’, which must contain two files inside: one is the ‘MasterPlate\_’, and the other is the ‘Drugmedia\_.csv’ file custom names can be appended after the underscore if desired. 
+
+**EASY**
+
+1. Architecture of the /EASY Subdirectory: 
+
+/EASY
+
+	/figs
+
+	/PTmats
+
+	datatipp.m
+
+	DgenNoGrowthResults200809.m
+
+	DMPexcel2mat\_2023winLinix.m
+
+	EASYconsole.fig
+
+	EASYconsole.m
+
+	NCdisplayGui.m
+
+	NCfitImCFparforFailGbl2.m
+
+	NCscurImCF\_3parfor.m
+
+	NCsingleDisplay.m
+
+	NIcircle.m
+
+	NImParamRadiusGui.m
+
+	NIscanIntensBGpar4GblFnc.m
+
+	p4loop8c.m
+
+	par4Gbl\_Main8c.m
+
+	par4GblFnc8c.m
+
+	
+
+1. To analyze a new Q-HTCP experiment:  
+1.  **Open the EASY Software**.  
+   1.  Open ‘EstartConsole.m’ with MATLAB  
+   1. Click the Run icon (play button)  
+   1. When prompted, click “Change Folder” (do not select “Add to Path”).  
+   1. In the pop-up display, select from the ‘File’ dropdown: ‘New Experiment’. From the pop-up, choose where to save the new file. Navigate to the relevant job in the ExpJobs folder, name the file accordingly, and click ‘save’. The newly created .mat file in the newly created Results folder will automatically be loaded. The file name will then be automatically appended by the code with the current date information (e.g. ‘A1.mat’ will become ‘Results2023-07-19A1)  
+      1. If the experiment has already been created, it can be reloaded by clicking ‘Load Experiment’ instead of ‘New Experiment’ and selecting the relevant results   
+   1. Next, in the pop-up display, click on the ‘Run’ dropdown menu and select ‘Image CurveFit ComboAnalysis’.  
+      1. In the updated pop-up, choose/highlight all desired image folders for analysis (this is generally all of the folders, since only the ones that need analysis should be there) and then click on ‘continue’. As the program is running, updates will periodically appear in the Command Window; there will be an initial pause at “Before call to NIscanIntens…..”.   
+      1. When the curve fitting is finished, the EASY console will pop back up. Check to see the completed analysis results in the newly created ‘PrintResults’ Folder, inside of the ‘Results’ Folder. Other folders (‘CFfigs’, ‘figs’, ‘Fotos’) are created for later optional use and will be empty. **\*\*NOTE:** The image analysis is completed independent of labeling the data (strains, media type, etc. Labeling happens next with the ‘GenReports’ function).  
+   1. Next, click on the ‘GenReports’ dropdown and select ‘DrugMediaMP Generate .mat’  
+      1. **\*\*NOTE:** The ‘MasterPlate’ and ‘DrugMedia’ files have very specific formats and should be completed from a template. Additionally, the Masterplate file must be exact (it must contain all and only the strains that were actually tested). For example, if only part of a library is tested, the complete library file must be modified to remove irrelevant strains.  
+      1. You will be prompted to first select the ‘MasterPlate’ file. You will need to *navigate* away from the working directory to get to it. It is fine for the ‘MasterPlate\_’ file to be .xlsx (or .xls), and if you don’t see it in the popup window, then change the file type from ‘.xls’ to “all files” and then select it. Once it is selected, a report of the number of master plates in the file will pop up; when the report appears, assuming it is correct, click on ‘OK’.  
+      1. You will then be prompted to select the ‘DrugMedia’ file from the relevant job folder. You will automatically return to the correct prior directory location. Choose it and click ‘OK’. You may see a warning about column headers being modified, but that’s ok.  
+         1. This will create an additional file in the ‘MasterPlatesFiles’ folder named ‘MPDMmat.mat’  
+   1. Finally, click on the ‘GenReports’ dropdown and select ‘Results\_Generate.’ 
+
+      
+
+      1. You will first see ‘\!\!ResultsElr\_.txt’ generated in the ‘PrintResults’ folder. Refreshing will reveal an increasing file size until you see the ‘\!\!ResultsStd\_.txt’ being generated. When finished, the ‘\!\!ResultsStd\_.txt’ will be about the same file size and it should be used in the following StudiesQHTCP analysis.    
+           
+      1.  ‘NoGrowth\_.txt’, and ‘GrowthOnly\_.txt’ files will be generated in the ‘PrintResults’ folder. 
+
+**System for Multi-QHTCP-Experiment Gene Interaction Profiling Analysis**
+
+1. **Introductory Remarks**
+
+“StudiesQHTCP” is a program that incorporates several command line scripts and provides a directory structure for input and output files.
+
+The analysis system involves Sean Santos’ R code for calculating genetic interaction values and z-scores, clustering of gene interaction z-scores using Recursive Expectation-Maximization clustering (REMc) which relies on WEKA and Java implementation, Go Term Finder (GTF) analyses of the REMc clusters which uses python. Jingu Guo worked on REMc and GTF code and Remy Cron incorporated it into a Java ‘.jar’ file to make it possible to run by multiple users from a shared folder. The executable ‘.jar’ files and all associated Python, Perl, and R scripts are executed via a single master shell script, *REMcMaster3.sh*. \[See section IV.7\]
+
+1. **System Requirements (software/packages necessary to run StudiesQHTCP)**  
+   1. Software \- These can all be downloaded from the respective online platforms for each operating system   
+      1. R  
+      1. Perl  
+      1. Java  
+      1. MATLAB  
+   1. Packages \- These packages must be installed in a specific order to ensure proper installation.
+
+   **For MacOS:** It is recommended that MacOS users download Homebrew for easy installation of the following packages. The command prompt to download Homebrew followed by the prompts to download the necessary packages are listed below. 
+
+export HOMEBREW\_BREW\_GIT\_REMOTE=[https://github.com/Homebrew/brew](https://github.com/Homebrew/brew)
+
+/bin/bash \-c "$(curl \-fsSL [https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh](https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh))"
+
+sudo cpan File::Map  
+sudo cpan ExtUtils::PkgConfig  
+sudo cpan GD  
+brew install graphiz  
+brew install gd  
+sudo cpan GO::TermFinder  
+brew install pdftk-java  
+brew install pandoc 
+
+	**For Linux:** The package manager commands used below are for Debian-based distributions. 
+
+If using Fedora or CentOS, you may need to use ‘dnf’ or ‘yum’ in place of ‘apt-get’
+
+		sudo cpan File::Map  
+sudo cpan ExtUtils::PkgConfig  
+sudo cpan GD  
+sudo apt-get install graphviz  
+sudo apt-get install libgd-dev  
+sudo cpan GO::TermFinder  
+sudo apt-get install pdftk-java  
+sudo apt-get install pandoc
+
+**For R:**  
+	install.packages(“BiocManager”)  
+	BiocManager::install(“org.Sc.sgd.db”)  
+	install.packages(‘ontologyIndex’, dep=TRUE)  
+install.packages(‘ggrepel’, dep=TRUE)  
+install.packages(‘tidyverse’, dep=TRUE)  
+install.packages(‘sos’, dep=TRUE)  
+install.packages(‘openxlsx’, dep=TRUE)
+
+	
+
+1. **Proper Architecture of Beginning Subdirectories**
+
+**/StudiesQHTCP**
+
+**StudiesDataArchive.txt**
+
+**/ExpStudy** (user named)
+
+**/A\_QHTCP Study Design and Notes**
+
+**/Code**
+
+22\_0602\_Remy\_DAmPsList.txt
+
+All\_SGD\_GOTerms\_for\_QHTCPtk.csv
+
+All\_SGD\_GOTerms.csv
+
+**/devStuff**
+
+InteractTemplateB4fixes.R
+
+InteractTemplateB4Prompt4SDinput.R
+
+gene\_association.sgd
+
+gene\_ontology\_edit.obo
+
+go\_terms.tab
+
+GTAtemplate.R
+
+ORFs\_w\_DAmP\_list.txt
+
+PairwiseLK.R
+
+Parameters.csv
+
+**/ScriptTemplates** {preserves starting templates of code modified by user}
+
+**/BU\_Legacy**
+
+	InteractTemplate.R
+
+Concatenate\_GTF\_results.py
+
+Concatenate\_GTF\_resultsB4REMcMaster2.py
+
+GTAtemplate.R
+
+InteractionTemplate230119.R
+
+JoinInteractExps.R
+
+JoinInteractExps3dev.R
+
+PairwiseK\_lbl.r
+
+PairwiseL\_lbl.R
+
+PairwiseLK.R
+
+Remy\_yor\_dF\_correlation\_study.R
+
+TSHeatmaps5dev2.R
+
+SGD\_features.tab
+
+SGD\_features.tab.txt
+
+**/Sscripts**
+
+18\_0205\_heatmaps\_zscores\_2SD\_color\_ARem\_Z\_lm.R
+
+22\_0603\_Remy\_Exlcude\_DAmPs.R
+
+cmd\_Doxo\_SumZScore\_Z\_lm\_Interaction\_d...alidationedit.R
+
+cmd\_ScoreAllGOTerms\_From\_Z\_lm\_V2.R
+
+Compare\_GTF\_Averages\_BetweenScreens\_lm\_Kvals\_v2.R
+
+Compare\_GTF\_Averages\_BetweenScreens\_lm\_Lvals\_v2.R
+
+Compare\_GTF\_Averages\_BetweenScreens\_lm\_v2.R
+
+GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_3terms\_V2.R
+
+GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_4terms\_aging.R
+
+GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_4terms\_V2.R
+
+GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_5terms\_V2.R
+
+GO\_list\_All\_ChildTerms\_lmZscore\_max100child\_Heatmaps\_V2.R
+
+ScoreAllGOTerms\_From\_Z\_lm\_V2.R
+
+StudyInfo.csv
+
+TSHeatmaps5dev2.R                
+
+**/Documentation**
+
+	**\*\*\*ADD IN SEAN’S MANUAL\*\*\***
+
+Jingyu\_REMc\_Instruction for clustering and...2013Mar.docx
+
+**/LegacyDocs**
+
+QHTCP Analysis SystemRev2.docx
+
+QHTCP Analysis SystemRev2a.docx
+
+QHTCP Analysis SystemRev2b.docx
+
+QHTCP Analysis SystemRev2b0.docx
+
+QHTCP Analysis SystemRev2c.docx     
+
+**/Exp1**
+
+**/backups**
+
+InteractTemplateB4Prompt4SDinput.R
+
+ExpFrontend.m
+
+Z\_InteractionTemplate.R
+
+Notes Exp1
+
+**/ZScores**
+
+**/Exp2**
+
+**/backups**
+
+InteractTemplateB4Prompt4SDinput.R
+
+ExpFrontend.m
+
+Z\_InteractionTemplate.R
+
+Notes Exp2
+
+**/ZScores**
+
+**/Exp3** 
+
+**/backups**
+
+InteractTemplateB4Prompt4SDinput.R
+
+ExpFrontend.m
+
+Z\_InteractionTemplate.R
+
+Notes Exp3
+
+**/ZScores**
+
+**/Exp4** 
+
+**/backups**
+
+InteractTemplateB4Prompt4SDinput.R
+
+ExpFrontend.m
+
+Z\_InteractionTemplate.R
+
+Notes Exp4
+
+**/ZScores**
+
+/**GTAresults**
+
+**/Exp1**
+
+**/Exp2**
+
+**/Exp3**
+
+**/Exp4**
+
+**/REMc**
+
+AddShiftVals2.R
+
+DconJG2.py
+
+GeneByGOAttributeMatrix\_nofiltering-2009Dec07.tab
+
+**/GTF**
+
+analyze\_v2.pl
+
+concatenate\_GTF\_Results.py
+
+gene\_association.sgd
+
+gene\_ontology\_edi.obd
+
+GOontologyPar.sh
+
+SeanEmailPython2
+
+SGD\_features.tab
+
+SGD\_features.tab.txt
+
+Terms2tsv\_v4.pl
+
+**/Component**
+
+analyze\_v2.pl
+
+concatenate\_GTF\_Results.py
+
+gene\_association.sgd
+
+gene\_ontology\_edi.obd
+
+ORF\_List\_DAmPs\_Only.txt
+
+ORF\_List\_Without\_DAmPs.txt
+
+ORFs\_w\_DAmP\_list.txt
+
+SGD\_features.tab
+
+SGD\_features.tab.txt
+
+terms2tsv\_v4.pl
+
+**/Function**
+
+analyze\_v2.pl
+
+concatenate\_GTF\_Results.py
+
+gene\_association.sgd
+
+gene\_ontology\_edi.obd
+
+ORF\_List\_DAmPs\_Only.txt
+
+ORF\_List\_Without\_DAmPs.txt
+
+ORFs\_w\_DAmP\_list.txt
+
+SGD\_features.tab
+
+SGD\_features.tab.txt
+
+terms2tsv\_v4.pl
+
+**/Process**
+
+analyze\_v2.pl
+
+concatenate\_GTF\_Results.py
+
+gene\_association.sgd
+
+gene\_ontology\_edi.obd
+
+ORF\_List\_DAmPs\_Only.txt
+
+ORF\_List\_Without\_DAmPs.txt
+
+ORFs\_w\_DAmP\_list.txt
+
+SGD\_features.tab
+
+SGD\_features.tab.txt
+
+terms2tsv\_v4.pl
+
+jingyuJava\_1\_7\_extractLib.jar
+
+JoinInteractExps3dev.R
+
+mComponent.sh
+
+mFunction.sh
+
+mProcess.sh
+
+Notes/ REMc, GTF\_Ontologies and Associated\_Heatmaps	
+
+ORF\_List\_DAmPs\_Only.txt
+
+ORF\_List\_Without\_DAmPs.txt
+
+ORFs\_w\_DAmP\_list.txt
+
+**/REMcHeatmaps**
+
+**/REMcHeatmapsWithHomolgy**
+
+17\_0503\_DAmPs\_Only.txt
+
+**/Homology**
+
+REMcHeatmaps\_Z\_lm\_wDAmPs\_andHomology\_221212.R
+
+Yeast\_Human\_Homology\_Mapping\_biomaRt\_18\_0902.csv
+
+REMcJar2.sh
+
+REMcJar2old.sh
+
+REMcMaster2.sh
+
+REMcMaster3.sh
+
+**/TermSpecificHeatmaps**
+
+{Note: The TSHeatmaps… .R contains a \*\*Table\*\* section near the start where is a default set of tables.  If the user wishes to use different tables, i.e. (All\_SGD\_GOTerms\_for\_... .csv) that should be modified and the TSH… . R script relabeled to reflect user modification and that is included in the/Code section.  Users should always write notes related to code modifications and study goals-strategies.
+
+**/test-DevStuff**
+
+Int4DoxGem.R
+
+InteractionTemplate230119cutdown4compareSSV6.R
+
+REMcMaster2Bad.sh
+
+As stated earlier, the user can add folders to back up temporary results, study-related notes, or other related work. However, it is advised to set up and use separate STUDIES when evaluating differing data sets whether that is from experiment results files or from differing data selections in the first interaction … .R script stage. This reduces confusion at the time of the study and especially for those reviewing study analysis in the future.
+
+1. **How-To Procedure: Execute a Multi-experiment Study**
+
+To begin, consider the goals of the study and design a strategy of experiments to include in the study. Consider the quality of the experiment runs using EZview to see if there are systematic problems that are readily detectable. In some cases, one may wish to design a ‘pilot’ study for discovery purposes. There is no problem doing that, just take a template study, copy and rename it as XYZpilotStudy etc. However, careful examination of the experimental results using EZview will likely save time in the long run. One may be able to relatively quickly run the interaction Z scores (the main challenge there is the user creation of customized interaction… .R code. I have tried to simplify this by locating the user edits near the top).
+
+**Preliminary Task**
+
+1. **Copy the Template directory structure and rename it for your study**.   
+   1. This directory contains the structure and code for analyzing a multi-experiment study. It contains the code templates and other reference files called by the scripts.
+
+The user specifies the arrangement of the data (in ‘**StudyInfo.csv**’) by assigning it to /Exp1, /Exp2, /Exp3, or Exp4, which is particularly relevant for clustering as results will be ordered left to right according to experiment number.
+
+A utility (**ExpFrontend.m**) was made for recording into a spreadsheet (‘**StudiesDataArchive.txt**’) the date and files used (i.e., directory paths to the \!\!Results files used as input for Z-interaction script) for each multi-experiment study. 
+
+**Experiment Specific Interaction Zscores generation**
+
+**2\. In your files directory, open the /Code folder, edit the ‘StudyInfo.csv’ file**
+
+1. Enter the desired Experiment names- ***\*\***order the names in the way you want them to appear in the REMc heatmaps; and make sure to run the front end programs (below) in the correct order (e.g., run front end in ‘exp1’ folder to call the \!\!Results file for the experiment you named as exp1 in the StudyInfo.csv file)*     
+   1. The GTA and pairwise, TSHeatmaps, JoinInteractions and GTF Heatmap scripts use this table to label results and heatmaps in a meaningful way for the user and others. The BackgroundSD and ZscoreJoinSD fields will be filled automatically according to user specifications, at a later step in the QHTCP study process.  
+      
+
+   **3\. Open MATLAB and in the application navigate to each specific /Exp folder, call and execute ExpFrontend.m by *clicking the play icon*. \*\*Use the “Open file” function from within Matlab; do not ‘double click’ on the file from the directory. When prompted, navigate *to the ExpJobs folder and the PrintResults folder within the correct job folder.* Repeat this for every Exp\# folder depending on how many experiments are being performed. The Exp\# folder must correspond to the StudyInfo.csv created above.**   
+     
+   **Note: Before doing this, it’s a good idea to compare the ref and non-ref CPP average and median values. If they are not approximately equal, then may be helpful to standardize Ref values to the measures of central tendency of the Non-refs, because the Ref CPPs are used for the z-scores, which should be centered around zero.**  
+   1. This script will copy the \!\!ResultsStd file (located in /PrintResults in the relevant job folder in /ExpJobs ***\*\***rename this \!\!Results file before running front end; we normally use the ‘STD’ (not the ‘ELR’ file)* chosen to the Exp\# directory as can be seen in the “Current Folder” column in MATLAB, and it updates ‘StudiesDataArchive.txt’ file that resides in the /StudiesQHTCP folder. ‘StudiesDataArchive.txt’ is a log of file paths used for different studies, including timestamps.
+
+Do this to document the names, dates and paths of all the studies and experiment data used in each study. Note, one should only have a single ‘\!\!Results…’ file for each **/Exp**\_ to prevent ambiguity and confusion. If you decide to use a new or different ‘\!\!Results…’ sheet from what was used in a previous “QHTCP Study”, remove the one not being used. NOTE:  if you copy a ‘\!\!Results…’ file in by hand, it will not be recorded in the **‘StudiesDataArchive.txt’** file and so will not be documented for future reference. If you use the ExpFrontend.m utility it will append the new source for the raw \!\!Results… to the **‘StudiesDataArchive.txt’** file.  
+
+As stated above, it is advantageous to think about the comparisons one wishes to make so as to order the experiments in a rational way as it relates to the presentation of plots.  That is, which results from sheets and selected **‘interaction … .R’**, user modified script, is used in /Exp1, Exp2, Exp3 and Exp4 as explained in the following section.
+
+**4\. In each /Exp\# folder, rename the Z\_InteractionTemplate.R script according to the experiment focus**
+
+1. Example:  Interaction, Experimenter Initials, Experiment Focus \--\> ‘int\_RM\_2PE.R’  
+   **5\. Open the renamed interaction script, and edit each one beginning at the ‘++BEGIN USER DATA SELECTION++’**  
+1. This is designed so that the data of interest for each experiment is appropriately selected from the \!\!Results…txt file  
+1. The user can edit, step through, and test the R script without running through the whole routine by observing the resultant data table created in RStudio.  
+   1. The **Z\_InteractionTemplate.R** script has a collection of code lines that have been used for prior analyses (generally to select data from various \!\!Results…txt files), which may be commented out (if not relevant), reused as needed, and/or modified for a new study. These include lines associated with the removal of ‘dAmps’, specific concentrations, and items described in the ‘Specifics’ and ‘Media’, i.e., information specific to a particular experiment design. There are also code lines to replace gene names ‘OCT1/YKL134C’ /’MAY24/YPR153W’ and that get converted to date format in excel, by using only the ORF name and to remove data rows with ‘Blank’ listed; these lines of code convenient to reuse. Hopefully, these code lines can be used, commented out, or adapted to aid the user in modifying this section to the specific data requirements of the study. As a new user data filter code is developed for each ‘Study’ (and vetted), those lines can be added to the InteractionTemplate230119.R code in the **/StudyTemplate** folders to aid in future studies.
+
+   **6\. Open a terminal, navigate to each /Exp\# folder, and execute the (customized) ‘Z\_InteractionTemplate\_…” script by using the command line below:**
+
+   
+
+   
+
+**Rscript RenamedInteractionTemplate.R \\\!\\\!Results… .txt**
+
+**\*\*need to change wording to choose SD of Delta\_Background to exclude Data from analysis.**  
+**\[1\] "Be sure to enter Background noise filter standard deviation i.e., 3 or 5 per Sean"**  
+**Enter a Standard Deviation value to noise filter \>\>**
+
+**\[1\] Enter Standard deviation value for removing data for cultures due to high background (e.g., contaminated cultures). Generally set this very high (e.g., ‘20’) on the first run in order NOT to remove data, e.g. ‘20’. Review QC data and inspect raw image data to decide if it is desirable to remove data, and then rerun analysis.**   
+**Enter a Background SD threshold for EXCLUDING culture data from further analysis:**
+
+1. The script will request for the user to input a ‘Background Standard Deviation Value’.  This Background value removes data where there is high pixel intensity in the background regions of a spot culture (i.e., suspected contamination). 5 is a minimum recommended value, because lower values result in more data being removed, and often times this is undesirable if contamination occurs late after the carrying capacity of the yeast culture is reached. This is most often “trial and error”, meaning there is a ‘Frequency\_Delta\_Background.pdf’ report in the /Exp\_/ZScores/QC/ folder to evaluate whether the chosen value was suitable (and if not the analysis can simply be rerun with a more optimal choice). In general, err on the high side, with BSD of 10 or 12…. One can also use EZview to examine the raw images and individual cultures potentially included/excluded as a consequence of the selected value. Background values are reported in the results sheet and so could also be analyzed there.. 
+
+	**(For new terminal users, directory navigation tips are described below)**
+
+1. To navigate to the directory one can use the directory GUI (in X2Go, use the GUI to navigate to desired operating directory and then from the ‘File’ menu, choose “Open in Terminal’)  
+1. Alternatively, navigate there through the terminal window: ‘pwd’ “prints the current working directory”, ‘ls’ “lists” the subfolders in the current directory. ‘cd’’ followed by the name of the ‘subdirectory’ will move down into it. “cd .. “ changes to the parent directory  
+1. The tab key can be used to autofill unique characters after typing the initial letters of a folder or file you wish to call. 
+
+The template structure above assists the user with organization and management of Q-HTCP files and provides a uniform directory structure to streamline reference across different users and experiments.
+
+Since we are systematically comparing perturbations, most Q-HTCP studies will consist of either 2 or 4 experiment subfolders. 
+
+The Zscores files are used for subsequent analyses, including REMc, GTA and Term Specific Heatmaps. These further analyses are described below and can be completed in any order and/or concurrently from separate terminals.
+
+***\*\*Annotate Files produced and comment out code that produces files that are obsolete or clutter.***
+
+**REMc**
+
+**7\. Navigate to the /REMc directory and run the following Rscript:**
+
+**\[jwrodger@hartmanlab REMc\]$ sh REMcMaster3.sh**
+
+1. There is a single shell script which will execute a series of shell script commands that were previously executed individually. To execute, open a terminal in the …/REMc folder and type the following.  
+1. The command line will request the user to enter a standard deviation multiplier (factor) that will filter the ZScore data accordingly for use with REMc. That value will also be stored to the **StudyInfo.csv** file where the user entered descriptive Labels in at the start of this entire QHTCP study.  Those labels are used throughout the process on all the graphics that are produced.
+
+   
+
+1. The REMcMaster3.sh script will execute the entire process in roughly thirty minutes to possibly an hour. REMcMaster3.sh script tasks are as follows:  
+   1. Joins the interaction Zscores into a table appropriate for the REMc jar file executable  
+      1. Execute the REMcJar2.sh which calls the java executable with the appropriate arguments  
+      1. Add shift columns back to the REMcRdy\_lm\_only.csv-finalTable.csv file to produce the "REMcWithShift.csv" file to be used to produce the REMc Heatmaps task.  
+      1. Execute **REMcHeatmaps\_zscores.R** contingent upon "REMcWithShift.csv" file being created.  
+      1. Execute REMcHeatmapsWithHomology/REMcHeatmaps\_Z\_lm\_wDAmPs\_andHomology\_221212.R that is located in …/REMc/REMcHeatmapsWithHomology/REMcHeatmaps\_Z\_lm\_wDAmPs\_andHomology\_221212.R .  This is a copy of the R script used by Denver and renamed with all the essential files it needs stored with it.  
+      1. Execute the process of GTF contingent upon "REMcRdy\_lm\_only.csv-finalTable.csv" being produced by "REMcJar.sh" (Step1).  This process involves several tasks as follows:  
+         1. Execute DconJG2.py to produce the /Process/REMcRdy\_lm\_only directory and files.  This is first created in the **../Process** folder and …  
+         1. Then copied to the **../Function** and **../Component** folders  
+         1. Next, contingent upon the production the  **../REMcRdy\_lm\_only** folder being made, the REMcMaster3.sh script executes the mProcess.sh, mFunction.sh and mComponent.sh tasks within the associated ontology directories. These ontology scripts call the Perl utilities and arguments from the respective ontology folders. These ontology scripts also execute the Concatenate\_GTF\_results.py script to produce the /Process/ProcessResults.txt, /Function/FunctionResults.txt and /Component/ComponentResults.txt output files.   
+   1. These files are concatenated with a Linux utility:   
+      **Pdftk REMcHeatmaps/\*.pdf \< output path-filename.pdf\>** 
+
+***\*\*Annotate Files produced and comment out code that produces files that are obsolete or clutter***
+
+**GTA related work**
+
+**8\. Navigate to the Code directory and open a terminal to run the following Rscript to produce the GTA results for each Exp\#:**
+
+**\[jwrodger@hartmanlab Code\]$ Rscript GTAtemplate.R**
+
+1. It will create the **/GTAresults/Exp\#** directories for the number of experiments for which you have produced Zscore\_interactions.csv and populate them with output files. The script ‘knows’ where to find the interaction files and where to put the results.
+
+ 
+
+**9\. Still in the /Code directory, run the following Rscript, entering two Exp\# files as input arguments to compare:**
+
+**\[jwrodger@hartmanlab Code\]$ Rscript PairwiseLK.R Exp1 Exp2**
+
+1. This script will perform both L and K comparisons for the specified experiment folders. Note this could just as easily have been Exp3 and Exp4 or even Exp1 and Exp3. The script ‘knows’ how to label the results as it has the StudyInfo.txt table to assign your labeling convention to the results. The code uses the naming convention of PairwiseCompare\_Exp’\#’-Exp’\#’ to standardize and keep simple the structural naming (where ‘X’ is either K or L and ‘Y’ is the number of the experiment GTA results to be found in **../GTAresult/Exp**\_). The GTA analysis is now complete. {FYI There are also individual scripts that just do the ‘L’ or ‘K’ pairwise studies in the ../Code folder.}
+
+ **Term Specific Heatmaps Production**
+
+**10\. Navigate to the /Code directory and run the following Rscript to produce the Term Specific Heatmaps:** 
+
+**\[jwrodger@hartmanlab Code\]$ Rscript TSHeatmaps5dev2.R**
+
+1. The Term Specific Heatmaps are produced directly from the  
+    **../ExpStudy/Exp\_/ZScores/ZScores**\_Interaction.csv file generated by the user modified interaction… .R  script. The heatmap labeling is per the names the user wrote into the StudyInfo.txt spreadsheet.  
+1. Verify that the All\_SGD\_GOTerms\_for\_QHTCPtk.csv found in **../Code** is what you wish to use or if you wish to use a custom modified version.  If you wish to use a custom modified version, create it and modify the TSHeatmaps template script (TSHeatmaps5dev2.R) and save it as a ‘TSH\_study specific name’.
+
+ 
+
+**\*\*Naming of  ‘StudiesQHTCP/Study/output files.** The resulting files produced in StudiesQHTCP folders have standard file names, which will be the same initially, across all studies. However, when the analysis is complete, and it may be desirable to move some of the results files outside of their native directories, and therefore useful to give them unique and recognizable names. Descriptive names can be added to all files by running two scripts from a terminal after navigating to the corresponding code directory:
+
+i. “sh RenameZscores\_GTAresults.sh” will add names provided in ‘StudyInfo.csv’ to files in the ‘Zscores’ subdirectory of the respective ‘Exp’ folder and to files in the ‘GTAresults’ folder.
+
+ii. “sh RenameREMcHtmaps\_GTFfiles.sh” will append the label given by the user when prompted to files in the ‘REMc’ and ‘TermSpecificHeatmaps’ folders.
+
+[https://weka.sourceforge.io/doc.dev/weka/clusterers/RandomizableDensityBasedClusterer.html](https://weka.sourceforge.io/doc.dev/weka/clusterers/RandomizableDensityBasedClusterer.html)
+
+\#setSeed-int- the above link is relevant to how REMc results are always the same (presumably because seed selection is non-random).
+
+**Questions to address / notes to incorporate here or elsewhere:**
+
+**We need full documentation for all of the current workflow. There are different documents that need to be integrated. This will need to be updated as we make improvements to the system.**
+
+**In Easy \-**   
+**MasterPlate\_ file must have ydl227c in orf column, or else it Z\_interaction.R will fail, because it can’t calculate shift values.**  
+**Make sure there are no special characters; e.g., (), “, ‘, ?, etc.;  dash and underscore are ok as delimiters**  
+**Drug\_Media\_ file must have letter character to be read as ‘text’.**
+
+**MasterPlate\_ file and DrugMedia\_ are .xlsx or .xls, but \!\!Results\_ is .txt.**
+
+**In Z\_interactions.R, does it require a zero concentration/perturbation (should we use zero for the low conc, even if it’s not zero), e.g., in order to do the shift correctly.**
+
+**Need to enable all file types** (not only .xls) as the default for GenerateResults (to select MP and DM files as .xlsx).
+
+Explore differences between the ELR and STD files \- 24\_0414; John R modified Z script to format ELR file for Z\_interactions.R analysis.
+
+To keep time stamps when transferring with FileZilla, go to the transfer drop down and turn it on, see [https://filezillapro.com/docs/v3/advanced/preserve-timestamps/](https://filezillapro.com/docs/v3/advanced/preserve-timestamps/) 
+
+Could we change the ‘MasterPlateFiles’ folder label in EASY to ‘MasterPlate\_DrugMedia’ (since there should be only one MP and there is also a DM file required?
+
+I was also thinking of adding a ‘MasterPlateFilesOnly’ folder to the QHTCP directory template where one could house different MPFiles (e.g., with and without damps, with and without Refs on all MPs, etc; other custom MPFiles, updated versions, etc)
+
+Currently updated files are in ‘23\_1011\_NewUpdatedMasterPlate\_Files’ on Mac (yeast strains/23\_0914…/)
+
+For EASY to report cell array positions (plate\_row\_column) to facilitate analyzing plate artifacts. The MP File in Col 3 is called ‘LibraryLocation’ and is reported after ‘Specifics’ in the \!\!Results.
+
+Can EASY/StudiesQ-HTCP be updated at any time by rerunning with updated MP file (new information for gene, desc, etc)- or maybe better to always start with a new template?
+
+Need to be aware of file formatting to avoid dates (e.g., with gene names like MAY24, OCT1, etc, and with plate locations 1E1, 1E2, etc)- this has been less of a problem. 
+
+In StudiesQHTCP folders, remember to annotate Exp1, Exp2, in the StudyInfo.csv file.
+
+Where are gene names called from for labeling REMc heatmaps, TSHeatmaps, Z-interaction graphs, etc? Is this file in the QHTCP ‘code’ folder, or is it in the the results file (and thus ultimately the MP file)?
+
+Is it ok for a MasterPlate\_ file to have multiple sheets (e.g., readme tab- is only the first tab read in)?  
+What are the rules for pulling information from the MasterPlateFile to the \!\!Results\_ (e.g., is it the column or the Header Name, etc that is searched? Particular cells in the DrugMedia file?).
+
+Modifier, Conc are from DM sheet, and refer to the agar media arrays. OrfRep is from MasterPlate\_ File. ‘Specifics’ (Last Column) is experiment specific and accommodate designs involving differences across the multi-well liquid arrays. ‘StrainBkGrd’ (now ‘Library location’) is in the 3rd column and reported after ‘Specifics’ at the last col of the ‘\!\!Results..’ file.
+
+Do we have / could we make an indicator- work in progress or idle/complete with MP/DM and after gen-report. Now, we can check for the MPDMmat.mat file, or we can look in PrintResults, but would be nice to know without looking there. 
+
+File\>\>Load Experiment wasn’t working (no popup to redirect). Check this again.
+
+**In EZview**:
+
+What do the File, Parameters and Tools dropdown menu items do?  
+What is the ‘Hide’ button for?  
+What is the ‘composite’ overlay good for?  
+What is the file that is used for the ‘Info’ function above the Gene Directory.  
+how to wand over labels \- how does that work in matlab?
+
+**In StudiesQHTCP:**
+
+**For front end, be more specific about where to navigate to find results file.**
+
+**ELR type file errors out \- needs to be produced in a compatible format.**
+
+**\*\*change wording to “choose SD for Delta\_Background to exclude spot culture growth curve data from interaction analysis”.**
+
+**GTF:**  
+Limit to smaller terms.  
+Enable sort by term size.
+
+There needs to be an annotated set of MasterPlate File templates. These could be numbered and annotated chronologically, and each experiment could specify which instance of the MP file template is used. When possible/ if necessary, the folders of plate images should be reordered rather than reordering the MP file. Each Exp, should have an ExpDesc spreadsheet in it indicating the Exp Design (summarizing what is expected in the \!\!Results file), based on the ‘MasterPlate\_’ and ‘DrugMedia\_’ files.
+
+Need to add Ref to Blank positions in the new library construction.
+
+In EZview:
+
+John R made a version that runs the original Guide, the ‘exported’, or the ‘migrated’ Forms of the program. A variety of versions didn’t work very well. The original program, with some improvements, seems to work the best. We should try to optimize it.
+
+{AppDesigner   
+ /mnt/data/EZview/EZview2023/EZviewDev23\_0921POSadaptedOnM4800\_wlapp  
+Use EZvStartup which calls EZviewGui\_7.mlapp
+
+GUIDE   
+/mnt/data/EZview/EZview2023/EZviewDev23\_0919POScleanup4Pub\_MigrationWorkingFileExport\_wlapp  
+Use the standard EZviewGui.m to start execution}
+
+Update from John R:  
+“There are four EZstartup \----.m files.  
+EZvStartup.m                       	\-Guide version  
+EZvStartup\_Export.m         	\-Exported file  migration version  
+EZvStartup\_mlappLaptop.m \-M4800 sized Laptop version  
+EZvStartup\_mlappServer.m  \-Server sized
+
+You can try them out at your convenience.    
+I have obviously not tried them out on a Mac Laptop.   
+Extra files etc. still there.  It's a hack and chop job but it seems to work.  
+Location:  
+/mnt/data/EZview/EZview2023/EZviewDev23\_1004POSadaptedOnM4800\_wlapp”
+
+**Suggestions to improve EZview appearance:**  
+Fix heatmap dimensions to match image dimensions. Is it possible to enable the user to adjust the heatmap dimensions (e.g. by dragging edges or corners to resize its window)?  
+Have an option to use a fixed heatmap scale across an experiment.  
+Check chronological experiments.  
+What does “SpotView” button do?  
+Can we add scrollbar to RFtab popup window so that it can be resized without losing view of table?
+
+For **StudiesQ-HTCP**, For GTF, we need to make sure that the correct ORFpool (e.g., with or without damps)  is being used. Can that be a selection step in the code, or an additional step to include it in the code (try, etc).
+
+The Library Locations for E rows in the MasterPlateFiles are being converted to exponentials in the \!\!Results files- needs to be text. One idea is to convert to text and/or use a delimiter. If they have a delimiter, perhaps prefix of ‘mp’ (converting to text) is not needed? e.g.,  ‘1\_E1’ instead of ‘mp1E1’?
+
+For TermSpecificHeatMaps, which list of GOterms did we use (see Sean’s manual, p. 14; ‘1.3 Term Specific Heatmaps’). Maybe we need a shorter, or more dedicated list.
+
+Compare our GTF to that of YeastMine to check for ‘correctness' of updated files?
+
+In Studies QHTCP new template, edit the StudyInfo.csv on the server in Libre, but leave it as a single column (or choose to open it with comma delimiter) and edit between the commas. But don’t convert in Excel (text to columns \>\> resave), since this deprecates the .csv format and code won’t run anymore and gives a data frame error after the STDEV for background. 
+
+\*\*Consider updating Z\_InteractionTemplate.R in the Studies\_QHTCP template folder if modifications are made for a particular study that could be useful for additional future studies. The idea would be to comment out the study-specific modifications and overwrite the existing program.
+
+StudiesQHTCP:  
+RF z-interaction plots don’t include RF2; RF1 only?  
+We may want to set different z-score cutoffs, based on the shape of the rank plot.  
+We want to calculate mean and median CPPs for Refs and Non-Ref cultures. May want to adjust REF data so that the median CPP values for Ref cultures are same or close to that to the Non-Ref cultures.  
+We should regress through the origin for the z-score interaction fitting.  
+Define NG(no growth), DB(?) and SM(?) on InteractionPlots.
+
+In MPfile\_templates, replace all YKL227C with YDL227C (120 instances); may only be in the file with Refs added to MPs.
+
+Update gene by go matrix in REMc (from Dec07 2009\) folder of StudiesQHCP.
+
+The ‘FrontEnd’ popup message when it is played should say “Select the \!\!Results File (in ‘ExpJobs’ folder)” to avoid confusion / remind about QHTCP structure.
+
+In REMcMaster3.sh, change prompt to ask for Z-score value (not standard deviation) for filtering analysis.
+
+Can REMc cluster Aniyia’s data (extract names in place of gene names) does it fail because it can’t do GTF, even though it should be able to simply cluster the Int\_Z-scores and label heatmaps (i.e., do this without doing GTF).
+
+Also, provide more detailed message when prompted to enter background level in  Z\_IntR.
+
+Output Z\_scoreInt file in same order as InteractionPlots.
+
+Check all folders in template for updated files; e.g., not just the ‘code’ folder, but also Exp1/2/3/4, REMc and GTF, etc..
+
+For EASY, need notification for successful completion after selecting drug media file with prompt ‘labeling complete, you may now generate report’.
+
+REMc error (BMHonly run):  
+![][image1]  
+   
+REMc- include LibraryLocation info in FinalTable.  
+This would come from looking up ‘OrfReplicate’ column in ‘MP File’, not ORF name column.  
+Useful to have demarcator between MP and WellPos, e.g., mp8\_B24 so that clusters can be analyzed for MP artifacts (more concise preferred format is 8\_B24).
+
+End of GTA in terminal:  
+![][image2]  
+ 
+
+Full GTA result BMHonly:  
+![][image3]  
+The one pdf that is made can’t be opened.
+
+When we leave out the DAmPs, we should probably still do the 2nd REF plate.
+
+I go the same result by running the REMcMaster3.sh whether I used Zscore cutoff of 2 or 1 (2PEonly Experiment). Note: I reran the script in the same folder.
+
+Need new strategy to check for plate artifacts \- calculate REF averages and Medians and compare to non-REF averages and Medians. If need to do corrections, correct by median since these are less impacted by outlier/tails of distributions.
+
+Heatmaps change to incorrect ones when the print heatmap function is used.
+
+Don’t we need a program to remove the template files and keep only the results after StudiesQHTCP.
+
+What is this step \- what package do we need to openxlsx- what failed as a result of not having it?  
+![][image4]
+
+**Appendix**
+
+Notes on the standard EASY coding structure (in Matlab):
+
+- **/figs**: a folder with two pintool map (‘PT’) files in it  
+- **/PTmats:** a folder with several .mat parameter files   
+- **Datatipp.m**: Matlab function to display small text boxes w/ info of a particular data point when the cursor is hovered over it  
+- **DgenNoGrowthResults200809.m**: script responsible for generating the \!\!Results files(Std & Elr)  
+- **DMPexcel2mat\_2023winLinix.m**: script that prompts users to select the MasterPlate and DrugMedia files  
+- **EASYconsole.fig**: This is the figure for the EASYconsole GUIDE. In order to edit the EASYconsole using the GUIDE functionality, enter ‘ guide(“EASYconsole.fig”) ‘ into the command window. Future versions of MATLAB may no longer allow GUIDE editing and APPDESIGNER must be used.   
+- **EASYconsole.m**: main EASY program script; large center for GUI functionality; created using GUIDE  
+- **EstartConsole.m**: starter script for EASY; calls EASYconsole.m   
+- **NCdisplayGui.m**: called when the user accesses the ‘CurveFit Display’ functionality on the console; responsible for the working GUI \[part 1\]  
+- **NCsingleDisplay.m**: assists in producing a functional GUI when accessing the ‘CurveFit Display’ functionality on the console \[part 2\]  
+- **NCfitImCFparforFailGbl2.m**: calls NCscurImCF\_3parfor.m; performs curve fitting and data analysis as a set of cultures given their time points and intensity values \>\> produces ‘FitResultsComplete.txt’ in PrintResults folder \[must work in conjunction with ImParamGui.m\]  
+- **NCscurImCF\_3parfor.m**: part of the computational process when using the ‘Image CurveFit ComboAnalysis’ functionality; performs growth curve fitting using a logistic growth model; performs the curve fit both with and without the early-late-r improved code; collects and consolidates data into structures and that is returned back up through the calling functions  
+- **NIcircle.m**: generates circular boundaries for image processing (are squares used instead?)   
+- **NImParamRadiusGui.m**: necessary for the ‘Image CurveFit ComboAnalysis’ computations.   
+- **NIscanIntensBGpar4**: major part of the computations for ‘Image CurveFit ComboAnalysis’  
+- **par4Gbl\_Main8c.m**: preallocation of data structures that are passed into descending function calls; calls the parallel processing parfor loop which loops through all the plate imaging folders  
+- **p4loop8c.m**: calls par4GblFnc8c.m and passes to it preallocated structures and other variables; passes back the analysis data in renamed structures based preallocated structures to calling script par4Gbl\_main8c.m  
+- **par4GBLFnc8c.m**: calls NIscanIntensBG4par4.m; compiles returned data; calls NCfitImCFparforFailGb12.m passing data obtained with NIscanIntensBG4par4.m to the curve fit function
+
+**For PinTool Functionality, the following scripts must be in the EASY folder:**   
+**\-** NdirectPTGui.m  
+\-NIPTdirectParmsGui.m   
+\-NIPTsearchParmsGui.m   
+\-NImapPT.m   
+\-NImapPTcentA.m   
+\-NImapPTcentroidSrc.m  
+\-NImapPTcentroidSrcCirc.m 
+
+Other code Notes:  
+\>The Toolbar for the EASYconsole is found in line 61 of EASYconsole.m \>\> ‘figure’ \= on; ‘none’ \= off  
+\>PlateMapPintool button functionality:  line 345 in EASYconsole.m
+
+![][image5]  
+![][image6]  
+![][image7]
+
+[image1]: 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>
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+[image2]: 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DGBP7YHR93HUbjqbTxtlsafrxMu1GrUAs/mVj0nLYT2WFv/Xjknt6eeb3PQNOT87YY1ZCEq/u8gJrUzi3kq61ktz8r9PbQywtPfFvLYHkweKuayuPIvXK6WdsIbotvUjpvJHyFzC3Fqa5Q4Jq+n/IgLq18Tb9BKgeVjq3eTVe6y900PRsBrK1CUNQUmvx6YedSJpWEtxHFcK6wlPpZ+oZ8iu1S4RiqFbwhlJcj4qIf0uwnVODY0Npsjfg2ekq9BmyZn4sHTaIE3t41CTCttwEeFtM1j0YXlbG2PV8ZGao4TCrwe0bBvGQ/2r2iU8BuxupxoOE3+F1kK7mdsounwSeAz/riEigIHcha9jsCYdXCXH8fJ0+VorhPwUSFo+Fxs3LEdW7eswacjOmuOE4zmGLokB46CCi+Bf5iQwDcd91OE72dsounwSeAJgiCIxw8SeIIgCINCAk8QBGFQSOAJgiAMCgk8QRCEQSGBJwiCMCgk8ARBEAaFBJ4gCMKgkMATBEEYFBJ4giAIg/JQBV5+SZmq/EE6/Hjh9zIWl+3DBG748ICIy1iLN9m73HWOKZ2gpLdF+opUj7lH1VevTewehNbxEi+CIB5ffBL48TvOoTrjA3T0vK3P0jkO+zeKrtwy7HNDRbkugTdZta+UfSA8JIEvy3xf+3pWFew1w3cTaj4n6jlgrya+Wz32NsMha87AoiknCOJxxyeBz9gZh4/S9mBqp1oLO2ahJlnYmTTWe6xMY9mnY+HGBF5p9adr4aayvmOvZg0XdqaVpXYc3L4ECc/ruR6ZNHZlerZ6LCf2el2ek1Lg/YInI6qjRa6ntNVTtr8hj73rvZVsh8ft2lgOytfAqvOrpQ2KE2tf26q2QuPneQm8ubVsz7c/IcxrTtS2drUCb0X+7GCPkFvgmtdLatP6AuYe3qqTF0EQjzv1C7ylC6YEmWF+ehL2HZkpVuIGDMpAakMGtaOTnsOP2gmKl6sNIJTOSMyJqTotyuOsE1xr+KFC42aj4/DDcvISXybwFYJoZp6FPf45qUzHdcmr/YJC0c2HuyVxNx91Pr5Sl1OOUuCZwQc39xi2XnLqqcsUQ7mDH53uwKJQP1h7Jorv8Fa3TRCEsahX4C3Bn+D0iRM4caIKP/3mFCv5IvBqRyc9hx+1UQgvVwu82kykWrZOu4vAq9xs9Bx+WE4agT/xPTJXpAo74UVimZ7rkrL9T5nAR6bV7ZbUQHSdckxNI/DNh66FfcUg9J2fo2mXIAjjUY/AW9EjsVD+Wt813i5+rbcNXoNTGbFegdjn1orPTMz9TWYERm/FYBu7UcxCXvES0RUmbt8+xHW3asSb161P4N3F8/Ginwl+3WbUKfD5V7IwkRlri56kTrSMzZBz+r76azmnPuyXm56cah/RNEffZJco2Lwe6wevp2w/5zc3LF3ikHt2E8IDzIhemIg3AiSnq54TvsSKcd01rjl3Q5k3M8/m5UqBtwbPRn/RYDsUi9x50nHPnCjngOH1DN6vDxZ9l4c8+1pNuwRBGI96BF5b4VHg5Yih6M4ErvlgxNbxC9H7aUigbH91xW7N8XvhfuZt8gvGx4ftSOipf1MkCMJYPJYC3//TTBwrd8NVVowWOscZ91Mole3vjQ/VHL8X7mfeO45X4nBCfzJFJognhMdS4AmCIIj6IYEnCIIwKCTwBEEQBoUEniAIwqCQwBMEQRgUEniCIAiDQgJPEARhUEjgCYIgDAoJPEEQhEEhgScIgjAoJPAEQRAGpcECz97u2Oj3pZjb4/z3Z3GqshhfhrfQHte1p2slfvbFek4Ns/77Nd3zat8mpM7+2wZiepD0Jsmm5oHZGPq97LujlaUbZufnIO5Zi/YYo975vjfubnXoGw/Dwevu6F0DjYfZMf5euZgsGZ9QfBL4HolOOD2uQ0zgc11bRScm5sIkuQ61Et2M7C6Xl6OREtuQRbAXn0eFU4hVuBcze/tpnInkc1X2dOyz8rW3/WftkesF+PVCkms/JvKLNGAC9roXyM5QVzwCz5yh7I5U0R2KO0OZzIEqZyZt3ixH1h7PMcDMTDnS8WXaYTgqquBa9bYgdEGIXZ8Pp7NMIfDeY8IdrbiDFHO04k5MSicsTfs6LlcsVp4jBzmOImTOH6StY7IiZP4R2NNSsTo5BVnFBUjub9N12eoTv1uMxeKwvnGBZ45W+92pYj80blXmDnh7VREObFuLPWW5ugKvP9+BSB4/DwePV6LiWLo8tmwupbHVz1sdW8LbCcvSZWaDHbzYHPji4KVdJzpjYtI6eGmcx4Q4rIzHYWUa5zE+fndx8GLzpHbwsnSZgaOly2Bq1g9fCLkndLfKbl3veF5fLWET82yKmwfx6NMogc/79QimdfXDqqojmNrBDEu3WejGTCqswZiV58b8l/RfR2t+ahJCRDMLCevzc9BPfK+5sCjLXHJ5fQJfWPaFXC+xRwDe3eVGcm8bIkOfgu3lRXDvlt5Vz94rX+kx5xDzrtmDMYFmLDsh5N9RyDt4jriz8esWB/sfbk2+PEfWHs+RvaM9/0874roKYuDfB0vKFPWs3WWBV4+JpZtwg3EnI1R8/3x3TGT1WY5vpPi0M1e+I99kG44hbEdm6YDXxr2lOZcJfK+kYjgTXkD54lC0n7gP9tndMD4uHJ2FG5ul0wyE+Elx1pd+LcZicYJZmSDw7z0ThkX2I0h4ubnYj5yj8WJf+Nxae86Fu2QBzB0nIftqvq7AM9TzzcSlJicRoS2lz3xs2VxKY2vTzVsdV8QvGNEKu0Y2v3HdhJuIcMOfJwihqWWo3N+jx5LF/rI54OM/bXuqOAdM4Hl/xVieemyM5HqqdaI3Jqyu+v3/hWXSzpnVY74FLA4r43FYWf4fdu+8PXW93v+vuk7YGqzL4MXkPwDLS7MwSVjfJr+eGBnRUyXmVjHPhngUEI8vjRL43MIE0WqPW+gxyzpmV+eox7JOfcHrORMx6hP4avlxjQ27YoTd1OdubI16FlVp76Hz6B1wL5Ze4asW+BzHHNH+j+dtG7Gp1hmqoFCTL8+Rtcdz3BXTzMtW760tF2rPVwi8eky4ZaF659Qogbd0QlXOZiR/OALd2+iJKxN4F/aMbYPDHwWhecwOOJN6aWwUWZwx6afEWHIcQeC5ZSEzetGzI/SP2I7q1HCPmYr+Dp6hnm82XocmB8Ls+czHlo2JNLYtdfNWx9WDzW+DLRoFfLFoVK8TvTFR58NQW0uyOKyMx2FlGmtJT927OXixNViXwI/eXIjsmSFN4i5GPP40SuD5M2jusMQcjZibEXtUoXQ0UqO+4PWciRj1CXxB2VK53iwm1ANXwiHspLvM2IGSskIsfcVPqqcWeFXe1hfmeTkzqfPlObL2eI6svfwr2dIOyRaC5JLabx5eO3jVmHBHK9lBin2FFnNco3Fh0kMp8Oa2r6KN+KikPT7I0nu0JAl8RmwrHJzWURZKtcsWizN1shSLxREfI4iPaCRHK1dyP123KuuLSSgpWQi/52Yg9/eG7eDTomvFkI8tm0tpbNkOXpu3Oq4ebH4b6uDF5sAXBy/1OtEbE5aDegevdh5jcVgZjyPu4FXOY7w/d3PwEte82sHL1h1T9hfCT2dsvKEd/JNEkwg8e7bpPi58tS4pwcHkoWiv9yzZpHfBtxKfLbLnorvj+qDZsGUoKC5GyZlfUVJchE3jOqGFUMY+/3v5tFzWNz5DrsfMK8xB05DztxvWkIWo+L+DmNTOLOyMhDhVv+DfK+eEek7Y9PJmO+GS73Bg82JsLHBo8uU5svZ4jqw9u3snVqdnIbe0EgUr3qw9VyHw6jExmVqiT9xuuMuKUVxRKj+3NQcMRXXlMbiPzpd21ar2bQMXyn1h/Uj/MBhWy7MoyrfD7ijCoZW1z21r0Rf4te4y5GakYe1HY5E3pz9sQpxRa78TY7E44u8l+C9Zmw/A58Xs5tUCIdN3iX2R55bdtNYV43DaUmxy5CK+S+MEno8tm0tpbPXzVsfVg63LtcKcHHUfR+Gqt2EOHCn3N8l+RuqvMAd8/LPmDRLnQP4lq9Df5L7N5HpsjOR6mnWiMyYm7TP4mI3H5HqfBrPn+53EMh6HleUe2+OVt941YFFdJ+KaF9bN8uIL4toRv2UM24Cav39GWVkZyo7lIfm1un53Qc/gnyR8EnjDYm7XpNZ75rZRiGmlLSfuP7J3r86xe6aR60RjLSnEYWU8Diur8y+yCKIJeLIFniAIwsCQwBMEQRgUEniCIAiDQgJPEARhUEjgCYIgDAoJPEEQhEEhgScIgjAoJPAEQRAGhQSeIAjCoJDAEwRBGBQSeIIgCIPyAAVecqppjDNTY+CuOA+qPYIgiEeNByjwEsrX/vrK/o3a9177gvo1w3o0NjZBEMSjzsMV+JahssMQc8/RdRhidXSMDZSwNwkyZxzuijPvBc+71hUCr3UPks6pLzZBEMTjysMVeGsn2WGIuefoOgyxOvWIsOgy5U4Cd8XZHiG9c1wp8Fr3oGY+xSYIgnhceagC7z98A4KZyUXrcdh7epXnnGYICh2J4ye+wZsep5z6RFgUeOdcMIEP33we23wQ+J2jSOAJgjA2D1XgmT0at5Bj9mi6FnLCeV7WZDqIj2iuZMm2Z/zxi9cjGo09nPQNob7YBEEQjysPXODvGf9QLF++vJYlExAaLNnxac4lCIJ4gnn8BF4H7hOrLicIgniSMYTAEwRBEFpI4AmCIAwKCTxBEIRBIYEnCIIwKCTwBEEQBoUEniAIwqCQwBMEQRgUEniCIAiDQgJPEARhUEjgCYIgDAoJPEEQhEGpR+BteH1FGS7+9j/877eLqK6uRjPPe1+6WbTB6qVVLMJb6ZSrYG+BVLow+eLMpIvVHy38pTdLNiV1998CP03Z3bF0jvPtdcVW6RXI9x2/lzEhQKdcD0s3zM7PQdyzte/u98LH+W4sZZnvo71ZW94QFpft872/TYAv883OYU5j6nJvbBiy5gxyZnTWOUYQEvUIvHRSj0QnnIk9YDV53r3u2ooVaUdQmhKFjqLQtYI97yjsLhcykwbjKZ2LzjZkEezF51HhFGIV7sXM3n4wmVvD6c5HvtuN/QlhtefWI/D9Z+2R6wX49UKSaz8m8os0YAL2uhdg3EYHXJU1uJIeKV5Qlq4zYXekYlFKKlwbYxDE8jYHorLUjoPbl2BDXoEmZxEhR9YezzFA6FuuOx1fph2Go6IKrlVvC0IXhNj1+XA6yzA9SHqHvXpMTKYW6D09HQ53EdzlxxAoxDG3i8aaihq4hDHJXxWJtjrjZrI8I/elVhhaIM+RgxxHETLnD9LWMVkRMv8I7GmpWJ2cgqziAiT3tyF6jR2lrgK4yp3iXLI4feJ3i7FYHNY3LvB+wZOx350q9qPvxzvFvshza+6At1cV4cC2tdhTlqsr8PrzHYjk8fNw8HglKo6ly2PL5lIaW/281bEl2qDYsybZZ0uXmVgmrElH+Sm41kSI48b7mzr2Obm/fPwPLRoqzgEXeNbfqI4WuR4bI7meZp3ojIlw3uspP+LC6tfkdcrHls0TG1u9+VbOCWuPn/PP5SrFmvCeb3GerC9g7uGteMfjmSBh82qfIBol8Hm/HsG0rn5YVXUEUzuYYek2C92swrnWYMzKc2P+S/q7ZvNTkxDCzvN8ZjZ6zELP1KyfaKPHy+sTeG69x+ol9gjAu7vcSO5tQ2ToU7C9vAju3bFSvcFfo3K7R+BZ3jV7MCbQjGUnhPw7CnkHz0GoTbi4u8XB/odbky/PkbXHc2Tvms//0464roIY+PfBkjJFPWt3WeDVY2LpJtxg3MkIZXn7dcdEVp/l+EZKvTs68TyhL/J5tuGiraHJ0kG0NlSfywS+V1IxnAkvoHxxKNpP3Af77G4YHxcuWySGMDtEPYtEQeDfeyYMi+xHkPByc7EfOUfjxb7wubX2nAt3yQKYO05C9tV8XYFnqOebCVBNTiJCW0qftTaKNt281XFF/IIR7fELYLD5jevmJ9s2MjtI3l9mB8n6y+aAj/+07aniHDCB5/0VY3nqcRtJsZ5qneiNCavbc8KXWDGuu3zT4WPL5onbT6rnWzknom2l5xxmRCP3VTXf0jz1xMiInioxt3q1TxCNEnj+iCLeXoA5wVb4R6ahmU5wNeoLnrksSQvUJros8fL6BJ47M7F6O0c1R1tBCHIXzEbclo1IWOzAt+MDpHoqgVfnzeKKF5slGJ8WFGry5Tmy9niOzAkq15mIHmI/bBix6Vzt+QqBV49JcxZnw3DN7kp9wdeFl8ALMNerKUv2iM5X6nMlgXchI7YVDk7rKLS9A86kXqjOjZcdtAayf8VzVQ5agsBnrhC+5TgXoV9zqR8/bIvw6ot/5HZUbw4X+it8e3Lr7+AZ6vlm45UWXfuoSeuy1VI3b3VcPdj88jlhrl7MLYz3l7mFsf6yOVCP/+IT38v9ZZ95Pe4yxuqp14nemOgjjS2bJ+5Opp5v5ZxwVzONwJu855vHIoj6aBKBt3SJQzhbdJYgRC9MxBt1LED1Bc9clpjDkqlZqOiyxMvrE3juzMTqMWcm28CVcAg76S4zdqCkrBBLX/GT6tUj8NYX5uFFP7Yzm4Gc3+rYwXucoHiOrL38K9mYJHwDMNlCkFxS+83DawevGhNL8CzkFS9BH3EH2QNx3T2uU4PX+OQopRR45nzFXK9M5vai85X63DoFXrjBcAetwTYpjsZBS3xE0xx9k11wJfcT+5F7dpPYFz631heTUFKyEH7PzUDu7w3bwSsFXuuyxXbw2rzVcfVg8ztR+DbJXb2YWxjvL3MLY/1lcyCP/7594hxIj2ik/jLB5vW4yxirp14nemPCclDv4PnYsnni7mTq+VbOCWuPn8Ocxvg56vnmsbTQDp7wpkkEnj0jdB8XvlqXlOBg8tA6f/GlveBbic9f2XPR3XF90GzYMhQUF6PkzK8oKS7CpnGd0EIoY5//vXxaLusbnyHXa8niBk1Dzt9uWEMWouL/DmJSOzNy3UKcql/w75VzQj0nbHp5WzqhquQ7HNi8GBsLHJp8eY6sPZ4ja8/u3onV6VnILa1EwYo3a89VCLx6TEymlugTtxvusmIUV5TKz23NAUNRXXkM7qPzFbvqWmwDF8p9Yf1I/zAYVsuzKMq3w+4owqGVEZo6dQn8WncZcjPSsPajscib0x82Ic6otd+JsVgc8fcS/JeszQfg82J282qBkOm7xL7Ic8tuWuuKcThtKTY5chHfpXECz8eWzaU0tvp5q+PqwdblWmFOjrqPo3DV2zAHjpT7m2Q/I/VXmAM+/lnzBolzIP+SVehvct9mcj02RnI9zTrRGROT9hk8H1s2T+LYmrTzrZwT1h6ry85ZXnyhdk2o5pvH0kLP4AlvfBJ449ISx8rdcAkX/d74UJ3jvhEYsw7u8uM4eboczXWOE/cfJvD6f9nUFDTNOiGIB80TLvAEQRDGhQSeIAjCoJDAEwRBGBQSeIIgCINCAk8QBGFQSOAJgiAMCgk8QRCEQSGBJwiCMCgk8ARBEAaFBJ4gCMKgkMATBEEYlAcr8Ob2OP/9WZyqLMaX4S3Eshaxe/Grx5SDvXzqrZWFKDlzmV6YRBAEcY88MIHXdfixPOPluiSfq3hdMHO4Ye42Shcc7qbD3JIkh6FAfJjtkJ2CJtXx+lqCIIgniQcm8Azt62O939kul6neB8/MD5THuZsOc0uS3HRsGL7hB9kpiCAIgnhMBf7qzydQ4HDAIbA2nL1f3IbXVp+FWdUeQRDEk8xjKfBauzQm8JWa9giCIJ5kHqrAM7cipetSSx1HJ4twHnO3UbrgcDcd5pYkuemQwBMEQah5oAJPEARBPDhI4AmCIAwKCTxBEIRBIYEnCIIwKCTwBEEQBoUEniAIwqCQwBMEQRgUEniCIAiDQgJPEARhUEjgCYIgDAoJPEEQhEEhgScIgjAoDRZ4y7NxyMmfjW4W7bF60XF08kbP0amV+PnS+mENdnnydotqOursv20gpgeZteVNAOtLU/dDF7+XMSFAp1wPSzfMzs9BXF0GK/XO970Rl7EWbzbTljeExWX7fO/vQ4Jdc3rrja9vdXl9tIndg1CbtpwwHvUIvA2vryjDxd/+h//9dhHV1dVodi8C3yoW4a10ylWoXxfMPld+03CBN1n90cKfGYLoHLsH6u6/BX6asrtj6Rznm3Bb2XvvdcqbmqYUeB/nu7GUZb7veZto43mcBb5x69uGIWvOiG9p1R4jjEY9Ai+d1CPRCWdiD1hN0mLLdW3FirQjKE2JQkdx4bWSLfQykwZLNnqqhnQt+8yt4XTnI9/txv6EsNpz6xH4/rP2yPUC/HohybUfE/lFGjABe90LMG6jw8sO0NJ1JuyOVCxKSYVrYwyCWN7mQFSW2nFw+xJsyCvQ5Cwi5Mja4zkGCH3Ldafjy7TDcFRUwbXqbUHoghC7Ph9OZ5liB+89JiZTC/Seng6Huwju8mMINEt2hGsqarzsCDXtW56R+1J7I2iBPEcOchxFyJw/SFvHZEXI/COwp6VidXIKsooLkNzfhug1dpS6CuAqd4pzyeL0id8txmJxWN+4wPsFT8Z+d6rYD26RKM+tuQPeXlWEA9vWYk9Zrq7A6893IJLHz5OtFfnYsrmUxlY/b3VsiTYo9qxJ9tnSZSaWCWvSUX4KrjUR4rjx/qaOfU7uLx//Q4uGinPABZ71N6qjRa7Hxkiup1knOmMinPd6yo+4sPo1eZ2GC99Geb2E56261pLKOWHtsX7YC1PFvrB+sOuLXXPseis4fka+5pTrWxoPaX0p+8bn1mudWF/A3MNbvcZSnTdhHBon8IUJeN5qQry9AHOCLfCP2iE7LLkrKz0uS9rG1O+Dbxazy7OobBi2vlour0/gq+XHNTbsihEuts/d2Br1LKrS3kPn0TvgXhwq1VOYiYiPlhxzEGypzds2YpMkmpZgzCko1OTLc2Tt8Rx3xTQTBD4JvcR+2PDWlgu151u7ywKvHpNmo3aieoP2W4jtjRSfdvCsL/J5lk6oytmM5A9HoHsbrbgyge+V5MKesW1w+KMgNI/ZAWdSL4Qv2IXvcg8hK7tUfK8+izMm/ZQYS44jCPz+zLOwxz8n7vJYP7iDFp9b/4jtqE4NF/rbA3Od+gLPUM83G69DkwNl5y0+tmxMpLFtqZu3Oq4ebH75nLy5WVhL1k5yfy9VrxL7y+ZAPf6LK9xyf8UyTz02Rryeep3ojYk6H0Z1WlRtvWDJWlLtPKacE9aeeH151hfrx/YIf7GMXW8mSxd57bK6fH2zn/n6UrbP57budUIYnUYJPH9EIS02K/wj01QOS/qoL/imEPido5qj7cR9yF0wG3FbNiJhsQPfjg+Q6qkFXpU3i8svwE99FPidowSBdyaih0dMRmw6V3u+UuBVY9KcxdkwXCMwjRJ4gaDQkZiyZA+On/hGcy4X+IzYVjg4raMslNW58Qhmwt56nCTw4rnNxFgszpsBZlHgM1cI33Kci9CvudQPtYOWf6Qg8JuZwAvfntwNE/i06FoxVAv8zlGSwKvzVsfVg80vn5PwzefhP3yD3N+9pyWhZnOgHv/FJ76X+8s+83psjHg99TrRGxM9qrlTmZfAexvTKOeEtScKvHOu2BfWj20egRcf0Vg6y2uX1VUKPF9f3jlIc1v3OiGMTpMIvKVLHMKZOFiCEL0wEW+wn3UaU1/w1uDZ6M9+SdYsFIvceXJ5fQJfULZUrjeL7cQHroSjzI0uM3agpKwQS1/xk+rVI/DWF+bhRT/h63m3Gcj5za3Jl+fI2uM5svbyr2RjUkehj7YQJJe4as9XCLx6TCzBs5BXvAR9WN5+PRDXnV+ka9Bap101SoE3t30VbcRHJe3xQZbeo6U6BF4QAH+TGYHRWzHYJsWZOlmKxeKIjxHERzTN0TfZBVdyP7EfuWc3iX3hc2t9MQklJQvh99wM5P6e32iB52PL5lIaW5tu3uq4erD5ndjB7PlW4UTL2Ay5v99Xfy32l82BPP779olzID2ikfrLBJvXY2PE66nXid6YsBx6TvgSK8Z1lx8buYvny/XqFHjFnLD2WD/yr2SJfWH9SOxh9Ung+fpS9o3Pbd3rREKdN2EcfBJ4w2Juh+7sgm8+GKsrdmuPNxBz2yjE3MdfKhJ1I4ugzrF7ppHr5OWIoXK9WB9/kavciKiPEURDebIF3tQSx8rdcJUVY2+89Ny+MQTGrIO7/DhOni5Hc53jxP3nvgp8I9dJ/08z5XotdI7rQQJPNCVPuMATBEEYFxJ4giAIg0ICTxAEYVBI4AmCIAwKCTxBEIRBIYEnCIIwKCTwBEEQBoUEniAIwqCQwBMEQRgUEniCIAiDQgJPEARhUB6gwEt2fI2x3iMIgiAazgMUeAkv6z3LM15uNj0Ti5A/O1g0mug2Kw+ueb1gVbk+Mdch+WVMls7iq1N9ckYiCIJ4wni4At8yFJ0FobZ0moGjx5KFf6cg2zFPdLOZk3cUcV0ssD4/B/3Ed7/3wxdlLvH92GqBZ7F8Nc4gCIJ4Uni4Am/t5GVXZjK3xeh0BxaF+sG+dyKeNmtdn5hlHgk8QRBE/TxUgWfuOUo3G/Ecvz5Y9F0ehjaXzlG7PjFHpewTS9HPTyhrE6Fwt/HNGYkgCOJJ4YEJfLNhy1BQXIx/L59GSXERNo3rBGvgSORmpGHtR2ORZD/jEX5mN1ei+EVsK/EZvMNdhN1xfdBSKBu9uRjFh3Zi67oFSPBY35kDhqK68hjcR+cr/EYJgiCeXB6YwPuMXzA+PmzXlhMEQRAN4tETeIIgCKJJIIEnCIIwKCTwBEEQBoUEniAIwqCQwBMEQRgUEniCIAiDQgJPEARhUEjgCYIgDAoJPEEQhEEhgScIgjAo9Qr8nTt3CIIgiMeM+y7w/2bE4OkJWZrypqCpYh9NHITnn+uBF0MGaI49KFg/rumUM25dSsHw17/ChVvaYwRBEHXhk8DfKElAcI8e6B4cjN5RyzRB7srNa/j32k1tuYJ3ph6sU9zuig+xfeHd56fgyN/a8rtx62JK4/PWQdkPdWxfBJ7NUY+AIHmeiv64rTmHIIgnC58F/uptocKNGuyK7YGbF1Zj+Ig1uHDzDla/EYYlJ2/iVvVKVFwXhOZCCoa2DcPKakmwrhfMRMhEzy77eg6m95+JO7cuo2jnYVTfkJII+UAhZjqx79y5jqDhy1Hxj3fyythMBN8IelfIsRRze4djwy+3MajvAq+c1J3nRA5YhMqb0s83z68U6s3Dsf+Ez/8dw/KzN3VjX8+L88qb980htKfsm5I9Y8OQfOI6fivPRFbF7wgbk4G/PcdYP3gsdWyx/aeFdn+4gU96hWPjZa14szkKidgiz9O7u3/XnEMQxJOFzwL/Usjz6NTxRUQuPIRrmePRvlNvDB4yBGEhIfj4yDVcPzpFEqRb1Vg5eJC+wN/6GbsnvIjkzUdx7q9bchJKMdOLzQT+7dQruK1KXi3ww4esFNq4iDVDBwvt30Lwe5leOak7z1EK/H97xyJ4ei6ui8euY+ze/3Rjq0WY963X8A+9+qakLCkMkzIvYdv4Xuj1XhrCFlTghudYfQI/bNAKnBdy5O2rY4s7+Lad5Xm6qHODIQjiycJngb964zy+HtoXnxT8jWtZ76Hr+9n4TxHo+pEPZTH9si6BF/kPm+a/i5d7f4Tv/pR2ol4CrxObCe17B5jQeyevEfjXv1KI8E0Ec9H05KSuz7mbwI/b5xF4VWy1CEv8h5qKg159U/LH9lEYtvgrTP7gG6R8MBWjd/wpH6tP4PkjGt6+Ora8g/fMk/o4QRBPHr4LvPDV/7+Sz9D3lWTc+lHYUfaYgiOCiB1YtBx5wr83T3+BUzfY45B1GN5O/xHN7T9c2Jjqwp3bV7ElcjBWnJfOeTE2A//nSUgvdmMFPmzAEq+c1PU5Xo9oqr/C4L6foVR8RFOB1cJuWS/29YJ4r7x53/4SxknZN698nXMwpG8Yxm3+GT+uG4/PSm7Ix7wEXhW7QQLvmafya9p+EgTxZOGTwKsr6XL7N5xjovi/AswKHYOMP3XOecAcz7Z75aQ+ThAEYWSaTuDv/IN+fcLwat8BiFlTgX81xx88pV9GeeWkPk4QBGFkmlDgCYIgiEcJnwU+tm0n9H51IIbOt2uCSPwPA18NQad2Y7G/zue/13HkgyD8mPIGOk0+4vlFpi9cx9CUi42oRxAE8eTis8CPCUmA+zqveB1/XSzBzoTBOOf55aTIdSc+CRl/V4EvmtUTf2yPQq/ZLkmob/+OfiG98VKPEIyYd1T4/CecyyIR+kIIJm07K/81y6gdf3nXIwiCIO5KowT+unspomPikbJ7fgMF/gZOJA/Gtf3vYeDik7h5h/2HRatxip3/XwnmvjwIN099jgGvLEDZv3cwodcopF9lf0VzAx9kX/Oqp41NEARBKGmUwHPYn+81TOD12Tc5GO0CnkPMptO4xf5M8eV45P1xGx8+H4iJ2do/jyQIgiDq55EQeMbN30uwZGg/4ee/UfHN+xjSfwBmjeyHBPd1zbkEQRBE/Tx0gb9e8Am+Z3Fv/YK0UcG4808l9qY7ceX2HYztNwO5/2rrEARBEPVzTwKvoRECTxAEQdwffBb42LbPInTwEAxfmK8JIvE/DBkcimfa3+3PJAmCIIgHhc8CTxAEQTxekMATBEEYFJ8Fvqn+QydWr1PYCkW962IcXnaj4ktE9O+Lvn37olv7nkhUvHGRIAiC8J1GCXxj/0MnXu+NQbUCz8pYHGUZZ1Tkapwl4wqCIIhG0SiB5zT2zySZO5GyHncs8op1+zI+K/5PU5cgCILwjUdU4K/DndBXY9FHEARB+M6jKfA3T2HJgDc19QiCIAjfaZTAy78IDemMl/q+gthvzkovAKtH4Hm9Nq2fketdE8pYHF4mxrmWjfe7T9XUJwiCIHzHZ4Gn/9CJIAji8cJngScIgiAeL0jgCYIgDEoTCvzfeKVnBzw7PVd2XDo8Z5Bcpj1fxa0L+Or14dryu/BvRgyenpClKScIgiB8FPhbP3yNhXGTMTEiDK9O3Sv+1cvw17/ChVt3sGboYKysvikGu37kQ4R8VCvwyjJ1wzK3LyPklRGIfv9jvNt3GNiNomztOAx9YwSiPi/A77dvouqLAR4XpxuoTO6LsKWnsXPqELwaEoRAWeD/xYn1ExDWpx/eXmgXXzfM4wx99VUxlqZtgiAIA+ObwAuCvvvKbdy5UYq5vcObVOBvnlmGyut3cPuXrYho/zpuXViN4SPW4MLNO1j9RhiWnLyJm+dW4CT7E8obx7Gw7xB8VX1Lil0wEyETJYG/deFrDA1LFs45hw0TJ2P7D7fkOHduVoux1G0TBEEYGZ8FvloQ8zu3LoqC3pQCfy17onT+zbNYNnAYrmWOR/tOvTF4yBCEhYTg4yPXxMc3iwWhv1GehL7D1+ESy4XFVgj8f/vGIXiadzs8zhBPLHXbBEEQRsZngWdizgX+9m/Cbrv3Zzh24w4mdX71ngSemWyzHfyN79dh2FPCDv7HFAzrMQVH/ryNA4uWI+9PZrp9B28NGo9xYaM9Jtye2ModfPVqvDFgvrCDP4uUUaOQcu6mHOfOrRoxlrptgiAII9Mogb9z62d8++EADHh7HKYOHYQVgpj+lzsXr/R8Gi06vIBXBryGKTt/xuABA+Qy9lnduIggvi/0eQNvvfcZpgxhz+D/ReX6sQh7eQBGJttx9bZ0XurIAATG7sEfns/DwoTYvTqiRWB3TNhcjZt3/kF5yhgMCO2PyC8K8btwHo8z8JVXxFiatgmCIAyMTwKvrkQQBEE8+pDAEwRBGBQSeIIgCINCAk8QBGFQDC3w3HVKzy2KIAjC6Bha4Bmad80TBEE8IZDAEwRBGBQSeIIgCINiaIHnrlPcLUp9nCAIwsgYWuAJgiCeZEjgCYIgDAoJPEEQhEG5J4FnjkrX5M8P3tGJIAiCqBufBP5GSYL4VsfbfzqQEPYqbvMAN695BWvM64JlSOAJgiCaFJ8F/ujnQ/FaggN/el7Xy2DvY6/dwdcv8NytiTssiW5NKkcndYIEQRBE4/BZ4F8ICkKfz9z4R1G5oQLP3Zq4wxJza1I7OqkTJAiCIBqHzwJfUmPHrJDe+CT/L7lygwU+6z10fT/bK4FrWVzgq/B5GAk8QRBEU+GzwLNn8P8UJ6H/Sx/JlRsq8NyOj1voMTs+tWWfOkGCIAiicfgk8OpKBEEQxKMPCTxBEIRBIYEnCIIwKD4JPCPjj59xwuWEfclQ8bOIbSCmB5nFny3PxqGbxVP+iNCmfxw2fbsHOzP2Y/fiN8Ucc/JnP8Q8W+GtlYUoOXMZl9YP0zku0eQ5Wrphdn4O4p61aI+ZmmP40jw4XZV4x7+23Ba+DhtjW+icL93w1WUEQTya+CTwuytXIMymrmyBn+fnexH4/RtHwl+nvEGY2yAsIROnzp5EztI30UHIZeXORLzcgh33Q7fp2+9J4C2d4+49Rw+28M2o/OZeBb45iqt/xI+XqlGwOkrnuIK7CrwH4WbNBd7cOgzuE5fw89kKlGZ+qjmXBJ4gHh8aLvCWIMSuz4fTWea1g1+RdgQFx8+gNCUKHQWBil5jR6mrAK5yJ6ysnjkQH2Y7cPB4JSqOpWOSIDjmdtH453IVXE4n8ldFImT+EdjTUnHInYWs4gIk97cJ7T0jx0kd+5wYy+5IxaKUVGzPdiNIaMvSZQaOli6DqVk/fCGcl9DdD19H9sGkNduxdUMqMrdloqWQY65rq5gnz1Edm+eYPH6emCfPcU1FjZxjW7NVznF1coqYJ4vD+8tztHSdKefo2hgjD7iXwHvqKfuW607Hl2mH4aioQgezdsIY1uBPMDrICpN/MCZ/6xbybo3+s/bA6c5HvtuNAFbP3AFvryrCgW1rsacsVxT4vh/vhD3vKOwuF55SxlYIfPN30tHmLbaDb4ew+d9q2iaBJ4jHh4YLPMfa3Uvgn7cKZZYuiLcXYE6wBeELduG73EPIyi7FQLGuDa+tPguzKoHKLbU7+F5JLuwZ2wZB0w6hecwOOJN6wWrtJMe5VL1KjJXjmINgUaA7i23xWKM3FyJ7ZgiamTti1ootiOtigW1ECtKmDkLProLAFyaIefIcTarYPMdDkwO98rS9kaLYwVvlHA9/FCTmyXLk/eU5sjHhObL25FhKgffUU/Yt152EXmwshVy2R/h7jZWEBV1n5cvfnhjNYnahev0w2MTPNuyKaQb/iO2oTg0X2uiBuU5J4K/+fAIFDgccAmvDFbEVAs++8XyRfQ5XfqqEY+sMTfsk8ATx+NBkAi8+VvCI2ZxgK6pz4xHM6rQepxD4Sk0CaoHPiG0liOdBWeBbDN8gx9l72iPw/DGGKPBWKVarUOyc8hJasp+FG01S6laMa21B57g0rBg6CIGdax/R8Bz9VbF5jmnR3sKqFnie48FpHcU8WY68vzxH9ZjIsRQCz9pn9ZR9y3UmoodH4LfpCrwJARMOoJnnZ3OrdhqB3zlKEPhIQeA3M4HvhSS3JPA/bIuQ63mhFHizn+cZfCBenJPrdSNhkMATxOPD/RP4DcMFUTQjMHorBt9F4E9lxKK152c9gW8TmyHH+b76azGWRuBt3TFlf6FCjNpi7opUzOjaBR8cOILEEXGwKZ7B8xxbqmLXKfCD18g56gk8y5H3l+eoHhM5lkLgWfusnrJv+VeyMamjMK62ECT28Ny8VFg6T8P4Z4Rj1iBEbamAX/BsFJQtRf9mwvFmoZglfDuxvpiEkpKF8HtuBnJ/zxcFPvfsJoQHmMXHbG+wf3lMWeD90H9pCVqIj2ha4ulxezw3jVpI4Ani8aHRAm9uG4WYVtqAjwppm8eiC8vZ2h6vjI3UHCcUeD2iob+iIQij4JPAZ/xxCRUFDuQseh2BMevgLj+Ok6fL0Vwn4KNC0PC52LhjO7ZuWYNPR3TWHCcYzTF0SQ4cBRVeAn83SOAJ4vHBJ4EnCA4JPEE8PpDAEw1C/V/LKf+rOYIgHi1I4AmCIAwKCTxBEIRBIYEnCIIwKCTwBEEQBoUEniAIwqCQwBMEQRgUEniCIAiDQgJPEARhUJiQr1u3zkvc2WdW/v8B4GATop923LEAAAAASUVORK5CYII=>
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+[image3]: 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>
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+[image4]: 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>
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+[image5]: 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>
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+[image6]: 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>
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+[image7]: 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>
\ No newline at end of file
diff --git a/workflow/docs/QHTCP - Hartman Lab User's Guide.odt b/workflow/docs/QHTCP - Hartman Lab User's Guide.odt
new file mode 100644
index 00000000..ef14693b
Binary files /dev/null and b/workflow/docs/QHTCP - Hartman Lab User's Guide.odt differ
diff --git a/workflow/script-run-workflow b/workflow/qhtcp-workflow
similarity index 99%
rename from workflow/script-run-workflow
rename to workflow/qhtcp-workflow
index 6c9ca1e4..8a895b02 100755
--- a/workflow/script-run-workflow
+++ b/workflow/qhtcp-workflow
@@ -190,14 +190,18 @@ parse_input() {
 }
 
 # @section Helper functions
-# @arg ALL_MODULES array A list of all available modules (that have been passed to module())
+# @arg $1 array A module to initialize (add to ALL_MODULES)
+# @set ALL_MODULES array A list of all available modules
 # @internal
 module() { 
   debug "Adding $1 module"
   ALL_MODULES+=("$1")
   declare -gA "$1"
 }
-# @arg ALL_SUBMODULES array A list of all available modules (that have been passed to module())
+
+# @arg $1 array A submodule to initialize (add to ALL_SUBMODULES)
+# @set ALL_SUBMODULES array A list of all available submodules
+# @internal
 submodule() { 
   debug "Adding $1 submodule"
   ALL_SUBMODULES+=("$1")
@@ -535,7 +539,7 @@ easy() {
   # TODO will need to play with the -sd startup option to see what works (well)
   # Skip this step altogether in auto mode since it requires graphical interaction
   if ! ((YES)) && ask "Start EASY in MATLAB? This requires a GUI."; then
-    export SCANS_DIR PROJECT_DATE EASY_DIR PROJECT_USER PROJECT_PREFIX EASY_SUFFIX EASY_RESULTS_DIR MASTER_PLATE_FILE DRUG_MEDIA_FILE
+    export SCANS_DIR PROJECT_DATE EASY_DIR PROJECT_USER EASY_NAME EASY_SUFFIX EASY_RESULTS_DIR MASTER_PLATE_FILE DRUG_MEDIA_FILE
     echo "Hit enter to use the default EASY results directory $SCANS_DIR/Results_${PROJECT_PREFIX}_($PROJECT_SUFFIX)"
     (( YES )) || read -r -p "Or enter a custom suffix: " suffix
     EASY_RESULTS_DIR="$SCANS_DIR/Results_${PROJECT_PREFIX}_${suffix:-$PROJECT_SUFFIX}"
@@ -549,7 +553,8 @@ easy() {
       fi
     done
     EASY_SUFFIX=${EASY_SUFFIX:-$suffix:-$PROJECT_SUFFIX}
-    EASY_RESULTS_DIR="$SCANS_DIR/Results_${PROJECT_PREFIX}_${EASY_SUFFIX}"
+    EASY_NAME="${PROJECT_PREFIX}_${EASY_SUFFIX}"
+    EASY_RESULTS_DIR="$SCANS_DIR/Results_$EASY_NAME"
     [[ -d $SCANS_DIR/MasterPlateFiles ]] || mkdir -p "$SCANS_DIR/MasterPlateFiles"
     DRUG_MEDIA_FILE="$SCANS_DIR/MasterPlateFiles/DrugMedia_$PROJECT.xls"
     MASTER_PLATE_FILE="$SCANS_DIR/MasterPlateFiles/MasterPlate_$PROJECT.xls"
@@ -1403,7 +1408,7 @@ main() {
   # Templates
   QHTCP_TEMPLATE_DIR="$SCRIPT_DIR/templates/qhtcp"
   STUDY_TEMPLATE_DIR="$QHTCP_TEMPLATE_DIR/ExpTemplate"
-  EASY_DIR="$SCRIPT_DIR/templates/easy"
+  EASY_DIR="$SCRIPT_DIR/apps/easy"
   IMAGES="${IMAGES:-"/mnt/data/ExpJobs"}" 
 
   DATE="$(date +%Y%m%d)" # change general date format here 
@@ -1468,8 +1473,8 @@ main() {
     SCANS_DIR="$IMAGES/$PROJECT"
     PROJECT_DATE="${PROJECT%"${PROJECT#????????}"}" # e.g. 20240723
     PROJECT_SUFFIX="${PROJECT#????????_*_}"
-    PROJECT_USER="${PROJECT#????????_}"
-    PROJECT_USER="${PROJECT_USER%%_*}"
+    PROJECT_USER="${PROJECT#????????_}"; PROJECT_USER="${PROJECT_USER%%_*}"
+    PROJECT_NAME="${PROJECT_DATE}_${PROJECT_USER}_${PROJECT_SUFFIX}"
     # Run selected modules
     for m in "${MODULES[@]}"; do
       ask "Run $m" && "$m"