From bcc2d875f25cfff8d3c27e7e884eb8009a8086ff Mon Sep 17 00:00:00 2001 From: Bryan Roessler Date: Sat, 27 Jul 2024 17:56:56 -0400 Subject: [PATCH] File refactoring --- workflow/.old/templates/qhtcp/Exp2/.DS_Store | Bin 0 -> 6148 bytes .../.old/templates/qhtcp/Exp2/ExpFrontend.m | 214 ++ .../templates/qhtcp/Exp2/ZScores/.DS_Store | Bin 0 -> 6148 bytes .../qhtcp/Exp2/Z_InteractionTemplate.R | 2730 +++++++++++++++++ .../InteractTemplateB4Prompt4SDinput.R | 2702 ++++++++++++++++ workflow/.old/templates/qhtcp/Exp3/.DS_Store | Bin 0 -> 6148 bytes workflow/.old/templates/qhtcp/Exp3/.Rhistory | 0 .../.old/templates/qhtcp/Exp3/ExpFrontend.m | 53 + workflow/.old/templates/qhtcp/Exp3/NotesExp3 | 0 .../qhtcp/Exp3/Z_InteractionTemplate.R | 2730 +++++++++++++++++ .../InteractTemplateB4Prompt4SDinput.R | 2702 ++++++++++++++++ workflow/.old/templates/qhtcp/Exp4/.DS_Store | Bin 0 -> 6148 bytes .../.old/templates/qhtcp/Exp4/ExpFrontend.m | 53 + workflow/.old/templates/qhtcp/Exp4/NotesExp4 | 0 .../qhtcp/Exp4/Z_InteractionTemplate.R | 2730 +++++++++++++++++ .../InteractTemplateB4Prompt4SDinput.R | 2702 ++++++++++++++++ workflow/.old/templates/qhtcp/REMcJar2.sh | 25 + workflow/.old/templates/qhtcp/REMcJar2old.sh | 10 + workflow/.old/templates/qhtcp/REMcMaster2.sh | 92 + workflow/.old/templates/qhtcp/REMcMaster3.sh | 121 + .../.old/templates/qhtcp/REMcRdy_lm_only.csv | 832 +++++ .../qhtcp/REMcRdy_lm_only.csv-WholeTree.txt | 3 + workflow/.old/templates/qhtcp/mComponent.sh | 32 + workflow/.old/templates/qhtcp/mFunction.sh | 32 + workflow/.old/templates/qhtcp/mProcess.sh | 37 + .../{templates => apps}/easy/DMPexcel2mat.m | 12 +- .../{templates => apps}/easy/DgenResults.m | 6 +- .../{templates => apps}/easy/EASYconsole.fig | Bin .../{templates => apps}/easy/EASYconsole.m | 189 +- .../{templates => apps}/easy/NCdisplayGui.m | 0 .../easy/NCfitImCFparforFailGbl2.m | 0 .../easy/NCscurImCF_3parfor.m | 0 .../easy/NCsingleDisplay.m | 0 workflow/{templates => apps}/easy/NIcircle.m | 0 .../easy/NImParamRadiusGui.m | 4 +- .../easy/NIscanIntensBGpar4GblFnc.m | 2 +- .../easy/PTmats/NCFparms.mat | Bin .../easy/PTmats/NImParameters.mat | Bin .../easy/PTmats/NPTdirectParameters.mat | Bin .../easy/PTmats/NPTmapDirect.mat | Bin .../easy/PTmats/NPTmapSearch.mat | Bin .../easy/PTmats/NPTsearchParameters.mat | Bin .../{templates => apps}/easy/PTmats/Nbdg.mat | Bin workflow/{templates => apps}/easy/datatipp.m | 0 .../easy/figs/NPTdirect.fig | Bin .../easy/figs/searchNPTIm.fig | Bin workflow/{templates => apps}/easy/p4loop8c.m | 0 .../{templates => apps}/easy/par4GblFnc8c.m | 0 .../{templates => apps}/easy/par4Gbl_Main8c.m | 1 - workflow/docs/.~lock.EASY Manual 17_0614.odt# | 1 + .../docs/20_0329_SS_Q_HTCP_Analysis_Readme.md | 491 +++ .../20_0329_SS_Q_HTCP_Analysis_Readme.odt | Bin 0 -> 1819969 bytes workflow/docs/EASY Manual 17_0614.odt | Bin 0 -> 7185425 bytes .../docs/QHTCP - Hartman Lab User's Guide.md | 821 +++++ .../docs/QHTCP - Hartman Lab User's Guide.odt | Bin 0 -> 1211272 bytes .../{script-run-workflow => qhtcp-workflow} | 19 +- 56 files changed, 19243 insertions(+), 103 deletions(-) create mode 100644 workflow/.old/templates/qhtcp/Exp2/.DS_Store create mode 100644 workflow/.old/templates/qhtcp/Exp2/ExpFrontend.m create mode 100644 workflow/.old/templates/qhtcp/Exp2/ZScores/.DS_Store create mode 100644 workflow/.old/templates/qhtcp/Exp2/Z_InteractionTemplate.R create mode 100644 workflow/.old/templates/qhtcp/Exp2/backups/InteractTemplateB4Prompt4SDinput.R create mode 100644 workflow/.old/templates/qhtcp/Exp3/.DS_Store create mode 100644 workflow/.old/templates/qhtcp/Exp3/.Rhistory create mode 100644 workflow/.old/templates/qhtcp/Exp3/ExpFrontend.m create mode 100644 workflow/.old/templates/qhtcp/Exp3/NotesExp3 create mode 100644 workflow/.old/templates/qhtcp/Exp3/Z_InteractionTemplate.R create mode 100644 workflow/.old/templates/qhtcp/Exp3/backups/InteractTemplateB4Prompt4SDinput.R create mode 100644 workflow/.old/templates/qhtcp/Exp4/.DS_Store create mode 100644 workflow/.old/templates/qhtcp/Exp4/ExpFrontend.m create mode 100644 workflow/.old/templates/qhtcp/Exp4/NotesExp4 create mode 100644 workflow/.old/templates/qhtcp/Exp4/Z_InteractionTemplate.R create mode 100644 workflow/.old/templates/qhtcp/Exp4/backups/InteractTemplateB4Prompt4SDinput.R create mode 100644 workflow/.old/templates/qhtcp/REMcJar2.sh create mode 100644 workflow/.old/templates/qhtcp/REMcJar2old.sh create mode 100644 workflow/.old/templates/qhtcp/REMcMaster2.sh create mode 100644 workflow/.old/templates/qhtcp/REMcMaster3.sh create mode 100644 workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv create mode 100644 workflow/.old/templates/qhtcp/REMcRdy_lm_only.csv-WholeTree.txt create mode 100644 workflow/.old/templates/qhtcp/mComponent.sh create mode 100644 workflow/.old/templates/qhtcp/mFunction.sh create mode 100644 workflow/.old/templates/qhtcp/mProcess.sh rename workflow/{templates => apps}/easy/DMPexcel2mat.m (93%) rename workflow/{templates => apps}/easy/DgenResults.m (96%) rename workflow/{templates => apps}/easy/EASYconsole.fig (100%) rename workflow/{templates => apps}/easy/EASYconsole.m (74%) rename workflow/{templates => apps}/easy/NCdisplayGui.m (100%) rename workflow/{templates => apps}/easy/NCfitImCFparforFailGbl2.m (100%) rename workflow/{templates => apps}/easy/NCscurImCF_3parfor.m (100%) rename workflow/{templates => apps}/easy/NCsingleDisplay.m (100%) rename workflow/{templates => apps}/easy/NIcircle.m (100%) rename workflow/{templates => apps}/easy/NImParamRadiusGui.m (94%) rename workflow/{templates => apps}/easy/NIscanIntensBGpar4GblFnc.m (97%) rename workflow/{templates => apps}/easy/PTmats/NCFparms.mat (100%) rename workflow/{templates => apps}/easy/PTmats/NImParameters.mat (100%) rename workflow/{templates => apps}/easy/PTmats/NPTdirectParameters.mat (100%) rename workflow/{templates => apps}/easy/PTmats/NPTmapDirect.mat (100%) rename workflow/{templates => apps}/easy/PTmats/NPTmapSearch.mat (100%) rename workflow/{templates => apps}/easy/PTmats/NPTsearchParameters.mat (100%) rename workflow/{templates => apps}/easy/PTmats/Nbdg.mat (100%) rename workflow/{templates => apps}/easy/datatipp.m (100%) rename workflow/{templates => apps}/easy/figs/NPTdirect.fig (100%) rename workflow/{templates => apps}/easy/figs/searchNPTIm.fig (100%) rename workflow/{templates => apps}/easy/p4loop8c.m (100%) rename workflow/{templates => apps}/easy/par4GblFnc8c.m (100%) rename workflow/{templates => apps}/easy/par4Gbl_Main8c.m (96%) create mode 100644 workflow/docs/.~lock.EASY Manual 17_0614.odt# create mode 100644 workflow/docs/20_0329_SS_Q_HTCP_Analysis_Readme.md create mode 100644 workflow/docs/20_0329_SS_Q_HTCP_Analysis_Readme.odt create mode 100644 workflow/docs/EASY Manual 17_0614.odt create mode 100644 workflow/docs/QHTCP - Hartman Lab User's Guide.md create mode 100644 workflow/docs/QHTCP - Hartman Lab User's Guide.odt rename workflow/{script-run-workflow => qhtcp-workflow} (99%) diff --git a/workflow/.old/templates/qhtcp/Exp2/.DS_Store b/workflow/.old/templates/qhtcp/Exp2/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..cae6fe4dc62ae792d8afb236d9a3da17b79ffcce GIT binary patch literal 6148 zcmeI0&u-H|5XQerL+wzIasbf_QY5aCXsT9F6`>Ns3t$8i|AIG;TC0xNvXe$Aio~TC zIPwAvt!{In<@U!bdAw&;{<>yx?ywnq}WWN>k^dK`=QF(v7 zIAWkX9hGRsdxrY-^3LaLU+*m&75&Ai?4TU&gRJK%0`#DfqmM$3i_Av*qOv3|h8r6{ zsIpSMusRjUbQs2IKR1Fn>Z>q+ z{v=K#J#On!8Wl!s9e&NPb?fVsNo(s?v$1)5Yuao~b~d-0jqRP*bXxPT-nhB@uzQ#c zQvFskP9zqWt)21C{5o@2B^&500duZ$0#<*${-JjpC+XT$bGbknC^ikym zcvZ7vZ+@PmAWsvW?n?!5oGa`t>93uuRXZGxe*y0g4ZE}NS^Gw;**XZ)0uUg@L44d( z@Hv*UWhq;hg80Z-=^n^R$+j3Ia*)VDA_s~5PaP%Oe2|SywnHHk9Uv1OAQRo;aGYdC zPSm;nKftN|zr*49kD+QO=(J5yUJlnNzB A>i_@% literal 0 HcmV?d00001 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 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+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$r.squared,3))) + + theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=16),axis.title.x = element_text(size=18), axis.text.y = element_text(size=16),axis.title.y = element_text(size=18)) + + + + + +dev.off() + + +#write.csv(Labels,file=paste("../Code/Parameters.csv"),row.names = FALSE) +timestamp() + +#BoneYard*********************************************** +#I'm thinking this parameter needs to be save somewhere "permanent' for the record so outputs can be reproduced. +#take this out of the Arguments. In Matlab I could for future in .mat file. Maybe I could save the SD Args[2] as part of the StudyInfo.txt. +#Corruptable but better than nothing. +#if(is.na(Args[2])){ +# std=3 +#}else { +# std= Arg[2] +#Delta_Background_sdFactor <- 2 #Args[3] +#DelBGFactr <- as.numeric(Delta_Background_sdFactor) +#} + diff --git a/workflow/.old/templates/qhtcp/Exp3/.DS_Store b/workflow/.old/templates/qhtcp/Exp3/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..ccdc65b6102c7bfbf585ab506f1e24c5f7f4bb9f GIT binary patch literal 6148 zcmeI0F;2rk5Jmr(ND0tHG*Hq}r%OQrkvV}oL;eWc6I_lR-1eQ%mK`xG1|u<9OtJ;6z&`V5I@HT73#dsiq!S8 zjWNP!H${p&WLUSKpDSp0Yp8fwB$+EK|4X8`mwra+;y5i_%6I1LtUmuZ{=6K@%72kn zJ+@bSS3lQCkip>z_sy%;SnUjTtSZxPM%(Jq&Fk{VYG zQNu9)9PSUNdbY5Vz-{}p!j|At}wV`LI1@yZw7VOVQf fPjAC8YjFD@K$=59q<= 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 0000000000000000000000000000000000000000..407f212ccf0cc8464b8326f3016471b842063e12 GIT binary patch literal 6148 zcmeI0F;2rk5Jmr(NJ*ec2PGFsk&Yq-MCJtUsK7P~61LVNN)H7mprJxsf+KJR&V&BW z8riWUQ6US>lcI6F^LKw_YiB0_WVOu~zzo0y8lx$C;W$4XqHyOBfcQDKsIbX5S&_O< zwlPHb?8ZoOgADi0=jRIA-5Dy@izIVp<$p=^^4!fRT^y%{OZm=x&7YPZ$DbE{S@|!r zs=MZD@9O6Y2{Jf5;I@9%YOAfGmQ`ik&S+YFoxh#^SZ&QyHmvT?YFRZ5!x%cE1}8^D zH)-#P@^0thgw&1oOt`vFS%ol-_hA-wWYy9WtU2`z^BmV|j; z4pBKo<&dyxNoYPKtSq5J5q31xs~qB2Hw*;kEW({s01W0`di1g3GlX@mmDWe?(3}YaC1L)FO;s5{u literal 0 HcmV?d00001 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 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+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]: + +[image2]: + +[image3]: + +[image4]: + +[image5]: + +[image6]: + +[image7]: + +[image8]: + +[image9]: + +[image10]: + +[image11]: + +[image12]: + +[image13]: + +[image14]: + +[image15]: + +[image16]: + +[image17]: + +[image18]: + +[image19]: + +[image20]: + +[image21]: + +[image22]: + +[image23]: + +[image24]: \ No newline at end of file diff --git a/workflow/docs/20_0329_SS_Q_HTCP_Analysis_Readme.odt 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