diff --git a/workflow/apps/matlab/easy/DgenResults.m b/workflow/apps/matlab/easy/DgenResults.m index df8940f9..99096a82 100755 --- a/workflow/apps/matlab/easy/DgenResults.m +++ b/workflow/apps/matlab/easy/DgenResults.m @@ -1,7 +1,5 @@ -%% CALLED BY EASYconsole.m %% -% Updated 240724 Bryan C Roessler to improve file operations and portability -% global projectScansDir +global projectName global matFile global defImParMat global printResultsDir @@ -27,7 +25,7 @@ end % MPnum=ImParMat(1); % opt=questdlg('Print Results Only (RES), DB Only (DB), or Both','Results Printout Options','Res','DB','Both','Both'); opt='Res'; -if ~exist('defImParMat','var') || isempty(defImParMat) % TODO needs better explanation +if ~exist('defImParMat','var') || isempty(defImParMat) % TODO needs explanation load(pointMapsFile); end destPerMP=ImParMat(2); % TODO this is weird, needs explanation @@ -37,7 +35,7 @@ numOfDrgs=length(DM.drug); numOfMeds=length(DM.media); destPerMP=numOfDrgs; -% TODO this should be rewritten or is uncessary, what is this for +% TODO this should be rewritten or is unnecessary, what is this for % Determine the longest set of intensity(tPts) for the Experiment Data Set maxNumIntens=0; for n=1:size(scan,2) @@ -53,22 +51,12 @@ end % end % TODO this seems weird -expNm=projectScansDir(max(strfind(projectScansDir,'/'))+1:end); drivePos=min(strfind(projectScansDir,'/')); drive=projectScansDir(1:(drivePos-1)); -DBupload=fullfile(drive,'EZdbFiles','DBupLOADfiles'); +% DBupload=fullfile(drive,'EZdbFiles','DBupLOADfiles'); -% Added to allow backward compatability -% Test for CFoutStd as indication of 2018version with r_refined fit code;If -% earlier version with only a standard composite fite, Print results to !!ResultsStd_...txt only -try - scan(1).plate(1).CFoutStd(1,1); - resultsFilename=fullfile(printResultsDir, strcat('!!ResultsELr_',expNm,'.txt')); - DBfilename=fullfile(printResultsDir, strcat('!!DbaseELr_',expNm,'.txt')); -catch - resultsFilename=fullfile(printResultsDir, strcat('!!ResultsStd_',expNm,'.txt')); - DBfilename=fullfile(printResultsDir, strcat('!!DbaseStd_',expNm,'.txt')); -end +resultsFilename=fullfile(printResultsDir, 'results_elr.txt'); +DBfilename=fullfile(printResultsDir, 'DbaseELr.txt'); if isequal(opt,'Res')||isequal(opt,'Both'),fid=fopen(resultsFilename,'w');end if isequal(opt,'DB')||isequal(opt,'Both'),fid2=fopen(DBfilename,'w');end @@ -271,7 +259,7 @@ for s=1:size(scan,2) end if isequal(opt,'DB')||isequal(opt,'Both') - fprintf(fid2,'%s\t %d\t %d\t %d\t %d\t %d\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t',expNm,s,p,r,c,n,selcode,drug,conc,media,mod1,conc1,mod2,conc2,orf,gene); + fprintf(fid2,'%s\t %d\t %d\t %d\t %d\t %d\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t',projectName,s,p,r,c,n,selcode,drug,conc,media,mod1,conc1,mod2,conc2,orf,gene); fprintf(fid2, '%.5f\t %.5f\t %.5f\t %.5f\t %.5f\t %.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f',... outCmat(n,1),riseTm,outCmat(n,3),outCmat(n,4),... outCmat(n,5),dbRsq,dbKup,dbKlo,dbrup,dbrlo,dbLlo,dbLup); %\t%.5f\t%.5f\t%.5f\t%.5f @@ -361,24 +349,24 @@ if isequal(opt,'Res')||isequal(opt,'Both') fclose(fid); end -if isequal(opt,'DB')||isequal(opt,'Both') - fclose(fid2); - try - copyfile(DBfilename,DBupload) - catch ME - fprintf('DB upload failed with error: %s\n', getReport(ME, 'basic')); - rep=sprintf('Failed copyfile to %s - %s', DBupload, rep); - errordlg(rep); - end -end +% if isequal(opt,'DB')||isequal(opt,'Both') +% fclose(fid2); +% try +% copyfile(DBfilename,DBupload) +% catch ME +% fprintf('DB upload failed with error: %s\n', getReport(ME, 'basic')); +% rep=sprintf('Failed copyfile to %s - %s', DBupload, rep); +% errordlg(rep); +% end +% end % Print results using the standard method try scan(1).plate(1).CFoutStd(1,1) %Test for 2018 r_refined version ln=1; - resultsFilename=fullfile(printResultsDir, strcat('!!ResultsStd_',expNm,'.txt')); - DBfilename=fullfile(printResultsDir, strcat('!!DbaseStd_',expNm,'.txt')); + resultsFilename=fullfile(printResultsDir, 'results_std.txt'); + DBfilename=fullfile(printResultsDir, 'DbaseStd.txt'); if isequal(opt,'Res')||isequal(opt,'Both'),fid=fopen(resultsFilename,'w');end if isequal(opt,'DB')||isequal(opt,'Both'),fid2=fopen(DBfilename,'w');end %121012 Combo if isequal(opt,'Res')||isequal(opt,'Both') %print Results @@ -553,7 +541,7 @@ try end if isequal(opt,'DB')||isequal(opt,'Both') - fprintf(fid2,'%s\t %d\t %d\t %d\t %d\t %d\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t',expNm,s,p,r,c,n,selcode,drug,conc,media,mod1,conc1,mod2,conc2,orf,gene); + fprintf(fid2,'%s\t %d\t %d\t %d\t %d\t %d\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t %s\t',projectName,s,p,r,c,n,selcode,drug,conc,media,mod1,conc1,mod2,conc2,orf,gene); fprintf(fid2, '%.5f\t %.5f\t %.5f\t %.5f\t %.5f\t %.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f\t%.5f',... outCmat(n,1),riseTm,outCmat(n,3),outCmat(n,4),... outCmat(n,5),dbRsq,dbKup,dbKlo,... @@ -649,20 +637,20 @@ try fclose(fid); end - if isequal(opt,'DB')||isequal(opt,'Both') - fclose(fid2); - try - copyfile(DBfilename,DBupload) - catch ME - fsprintf('DB upload failed with error: %s\n', getReport(ME, 'basic')); - rep=sprintf('Failed copyfile to %s - %s\n', DBupload, rep); - errordlg(rep) - end - end + % if isequal(opt,'DB')||isequal(opt,'Both') + % fclose(fid2); + % try + % copyfile(DBfilename,DBupload) + % catch ME + % fsprintf('DB upload failed with error: %s\n', getReport(ME, 'basic')); + % rep=sprintf('Failed copyfile to %s - %s\n', DBupload, rep); + % errordlg(rep) + % end + % end - msgbox([sprintf('Printing Script complete. Check !!Results sheets in %s for results.', printResultsDir)]) + msgbox([sprintf('Printing script complete. Check results sheets in %s for results.', printResultsDir)]) catch ME - fprintf('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/apps/matlab/easy/EASYconsole.m b/workflow/apps/matlab/easy/EASYconsole.m index 7e4543dd..95ef6ce4 100644 --- a/workflow/apps/matlab/easy/EASYconsole.m +++ b/workflow/apps/matlab/easy/EASYconsole.m @@ -141,8 +141,7 @@ function varargout = EASYconsole(varargin) if ~isempty(getenv('MASTER_PLATE_FILE')) 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 + else % try to find MasterPlate_ file on our own mp=fullfile(easyResultsDir, strcat('MasterPlate_', projectName,'.xlsx')); if exist(mp, 'file') masterPlateFile=mp; @@ -281,7 +280,7 @@ function NewExpDat_Callback(~, ~, ~) % Create a new experiment try - questdlg('\fontsize{20} NAME the file and NAVIGATE to the directory with the image folders.','File Creation','OK', struct('Default','OK','Interpreter','tex')); + questdlg('Name the file and navigate to the directory with the image folders.','File Creation','OK', struct('Default','OK','Interpreter','tex')); [matfile,easyResultsDir]=uiputfile('.mat'); inputFileName=strrep(inputFile,'.mat',''); diff --git a/workflow/apps/matlab/easy/NIscanIntensBGpar4GblFnc.m b/workflow/apps/matlab/easy/NIscanIntensBGpar4GblFnc.m index b4263689..7e040ac9 100755 --- a/workflow/apps/matlab/easy/NIscanIntensBGpar4GblFnc.m +++ b/workflow/apps/matlab/easy/NIscanIntensBGpar4GblFnc.m @@ -791,7 +791,7 @@ function [Tmpsbdg2, scanIntens, F_spots, bmtp, optomizedPos, TmpexpScanIntens2, else refBG(1)=refP(1)-floor(0.5*(refP(1)-(refPrR(1)+widthEx))); end - refBG(2)=refP(2)-floor(0.5*(refP(2)-(refPrC(2)+widthEx))); % return from lunch fix start here!!!!! + refBG(2)=refP(2)-floor(0.5*(refP(2)-(refPrC(2)+widthEx))); if rem(r,numRows)==0 refBG(3)=refP(1)+widthEx+ floor(0.5*(plateBotSpace)); else diff --git a/workflow/apps/matlab/easy/par4Gbl_Main8c.m b/workflow/apps/matlab/easy/par4Gbl_Main8c.m index 72be05a8..083b752f 100755 --- a/workflow/apps/matlab/easy/par4Gbl_Main8c.m +++ b/workflow/apps/matlab/easy/par4Gbl_Main8c.m @@ -1,5 +1,3 @@ -%% CALLED BY EASYconsole.m %% - global SWgrowthArea global scLst global ImParMat @@ -62,9 +60,9 @@ end % after each run to allow previous version of EASY to access data made % by EASY versions after 20_0819. try - load(fullfile(fotosResultsDir,'Nbdg')); %Modified to load from 'Fotos' 20_0819 + load(fullfile(fotosResultsDir,'Nbdg')); % modified to load from 'Fotos' 20_0819 catch - load(fullfile(pointMapsResultsDir,'Nbdg')); %Left in to accomodate loads of work before 20_0819 + load(fullfile(pointMapsResultsDir,'Nbdg')); % left in to accomodate loads of work before 20_0819 end % Load Fotos stored data fotosToLoad={'Coordinates', 'BGatTpts', 'anlZones', 'NCFparms'}; @@ -211,11 +209,11 @@ end % Save data in .mat files save(matFile,'scan'); -% save((fullfile(easyResultsDir,'PTmats','Nbdg')), 'sbdg'); %legacy location can probably get rid of in time +% save((fullfile(easyResultsDir,'PTmats','Nbdg')), 'sbdg'); % legacy location can probably get rid of in time save((fullfile(fotosResultsDir,'Nbdg')), 'sbdg'); -save((fullfile(fotosResultsDir,'Coordinates')),'FexpScanSpots') %Saves frames at each tPt +save((fullfile(fotosResultsDir,'Coordinates')),'FexpScanSpots') % saves frames at each tPt save((fullfile(fotosResultsDir,'BGatTpts')),'FexpScanBMtp') -save((fullfile(fotosResultsDir,'anlZones')),'anlZoneRefs')%Saves anl Positions at each tPt +save((fullfile(fotosResultsDir,'anlZones')),'anlZoneRefs') % saves anl Positions at each tPt % Print FitResults fileExt='.txt'; diff --git a/workflow/apps/r/interactions.R b/workflow/apps/r/interactions.R index a3c5d842..3d34190f 100644 --- a/workflow/apps/r/interactions.R +++ b/workflow/apps/r/interactions.R @@ -1,25 +1,22 @@ #!/usr/bin/env Rscript -# Based on InteractionTemplate.R which is based on Sean Santos's Interaction_V5 script -# -# Updated 240724 Bryan C Roessler to improve file operations and portability -# NOTE: The script now has 4 additional OPTIONAL arguments: -# 1. Path to input file +# NOTE: The script now has 6 optional arguments: +# 1. Path to input easy results file # 2. /output/ directory # 3. Path to StudyInfo.csv # 4. Path to SGDgeneList # 5. Standard deviation value +# 6. The experiment number (Exp# directory) -# Load libraries -library("ggplot2") -library("plyr") -library("extrafont") -library("gridExtra") -library("gplots") -library("RColorBrewer") -library("stringr") -library("gdata") -library("plotly") -library("htmlwidgets") +library(ggplot2) +library(plyr) +library(extrafont) +library(gridExtra) +library(gplots) +library(RColorBrewer) +library(stringr) +library(gdata) +library(plotly) +library(htmlwidgets) # Parse arguments args <- commandArgs(TRUE) @@ -56,43 +53,49 @@ if (length(args) >= 5) { delBGFactor <- readLines(file("stdin"), n = 1L) } delBGFactor <- as.numeric(delBGFactor) -if(is.na(delBGFactor)){ +if (is.na(delBGFactor)) { delBGFactor <- 3 # Recommended by Sean } -print(paste("The Standard Deviation Value is: ", delBGFactor)) +print(paste("The Standard Deviation Value is:", delBGFactor)) -outDir_QC <- paste(outDir,"QC/",sep="") +# Set experiment # +if (length(args) >= 6) { + expNumber <- args[6] +} else { + # User prompt for std multiplier Value + print("Enter the experiment number (Exp# directory)") + expNumber <- readLines(file("stdin"), n = 1L) +} +expNumber <- as.numeric(expNumber) -if (!file.exists(outDir)){ +outDir_QC <- paste(outDir, "QC/", sep = "") + +if (!file.exists(outDir)) { dir.create(file.path(outDir)) } -if (!file.exists(outDir_QC)){ +if (!file.exists(outDir_QC)) { dir.create(file.path(outDir_QC)) } -# Capture Exp_ number,use it to Save args[2]{std}to Labels field and then +# 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=studyInfo,stringsAsFactors = FALSE) # sep= "," -expNumber <- as.numeric(sub("^.*?(\\d+)$", "\\1", getwd())) -Labels[expNumber,3] <- delBGFactor -write.csv(Labels,file=studyInfo,row.names = FALSE) +Labels <- read.csv(file = studyInfo, stringsAsFactors = FALSE) # sep = "," +Labels[expNumber, 3] <- delBGFactor +write.csv(Labels, file = studyInfo, row.names = FALSE) -# BEGIN USER DATA SELECTION SECTION +# Begin User Data Selection Section # Read in the data -X <- read.delim(inputFile,skip=2,as.is=T,row.names=1,strip.white=TRUE) -X <- X[!(X[[1]]%in%c("","Scan")),] -#X <- X[!(X[[1]]%in%c(61:76)),] #Remove dAmp plates which are Scans 61 thru 76 +X <- read.delim(inputFile, skip = 2, as.is = TRUE, row.names = 1, strip.white = TRUE) +X <- X[!(X[[1]] %in% c("", "Scan")), ] +# X <- X[!(X[[1]]%in%c(61:76)), ] #Remove dAmp plates which are Scans 61 thru 76 +# X <- X[which(X$Specifics == "WT"), ] +# X_length <- length(X[1, ]) +# X_end <- length(X[1, ]) - 2 +# X <- X[, c(1:42, X_end:X_length)] -#X <- X[which(X$Specifics == "WT"),] - -#X_length <- length(X[1,]) -#X_end <- length(X[1,]) - 2 -#X <- X[,c(1:42,X_end:X_length)] - - -#use numeric data to perform operations +# Use numeric data to perform operations X$Col <- as.numeric(X$Col) X$Row <- as.numeric(X$Row) X$l <- as.numeric(X$l) @@ -103,342 +106,469 @@ X$AUC <- as.numeric(X$AUC) X$LstBackgrd <- as.numeric(X$LstBackgrd) X$X1stBackgrd <- as.numeric(X$X1stBackgrd) -#Sometimes the an experimenter may have placed the non-varying drug in the 'Drug' col instead of the 'Modifier1' col -#as was the case in Gemcitabin and Cytarabin experiments. -#The following allows user to rename columns so as to get the appropriate -#data where it needs to be for the script to run properly. -#colnames(X)[7] <- "Modifier1" -#colnames(X)[8] <- "Conc1" -#colnames(X)[10] <- "Drug" -#colnames(X)[11] <- "Conc" +# Sometimes the non-varying drug is in the 'Drug' col vs the 'Modifier1' col +# as was the case in Gemcitabin and Cytarabin experiments. +# The following allows user to rename columns so as to get the appropriate +# data where it needs to be for the script to run properly. -#set the OrfRep to YDL227C for the ref data -X[X$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$Drug != "BMH21",] #This removes data concerning BMH21 for this experiment +# colnames(X)[7] <- "Modifier1" +# colnames(X)[8] <- "Conc1" +# colnames(X)[10] <- "Drug" +# colnames(X)[11] <- "Conc" -#Mert placed the"bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. -#This code removes those spots from the data set used for the interaction analysis. Dr.Hartman feels that these donot effect Zscores significantly and so "non-currated" files were used. -#try(X <- X[X$ORF != "bad_spot",]) -#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++= +# Set the OrfRep to YDL227C for the ref data +X[X$ORF == "YDL227C", ]$OrfRep <- "YDL227C" -#get total number of drug concentrations -Total_Conc_Nums <- length(unique(X$Conc)) +# Sean removes the Doxycyclin at 0.0ug.mL so that only the Oligomycin series with Doxycyclin of 0.12ug/mL are used. +# That is the first DM plates are removed from the data set with the following. +# This removes data with dox == 0 leaving gene expression on with four different concentrations of Gemcytabin +# X <- X[X$Conc1 != "0ug/ml", ] +X <- X[X$Drug != "BMH21", ] # this removes data concerning BMH21 for this experiment -#function to ID numbers in string with characters+numbers (ie to get numeric drug conc) -numextract <- function(string){ +# Mert placed the "bad_spot" text in the ORF col. for particular spots in the RF1 and RF2 plates. +# This code removes those spots from the data set used for the interaction analysis. +# Dr.Hartman feels that these donot effect Zscores significantly and so "non-curated" files were used. +# try(X <- X[X$ORF != "bad_spot", ]) + +# Get total number of drug concentrations +total_conc_nums <- length(unique(X$Conc)) + +# Function to ID numbers in string with characters+numbers (ie to get numeric drug conc) +numextract <- function(string) { str_extract(string, "\\-*\\d+\\.*\\d*") -} +} -#generate a new column with the numeric drug concs +# Generate a new column with the numeric drug concs X$Conc_Num <- as.numeric(numextract(X$Conc)) -#Generate new column with the numeric drug concs as factors starting at 0 for the graphing later + +# Generate new column with the numeric drug concs as factors starting at 0 for the graphing later X$Conc_Num_Factor <- as.numeric(as.factor(X$Conc_Num)) - 1 -#Get the max factor for concentration +# Get the max factor for concentration MAX_CONC <- max(X$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. -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 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 + +# 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 overwrite the RF1&RF2 geneNames +# in the Z_lm_L, K, r&AUC output values. Values correlated well but were off by a multiplier factor. +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] + 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) +Xblankreplace <- X +# X = Xbu # for restore testing restore X if geneName 'Match' routine needs changing -#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 -# *********** +# 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 -#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ -#++++++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") { +# 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") - )) - + (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(...){ +scale_fill_publication <- function(...) { library(scales) - discrete_scale("fill","Publication",manual_pal(values = c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")), ...) - + 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")), ...) - +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") - )) - +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_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")), ...) - +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 +# 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 +# Begin QC Section +# Part 2 - Quality control +# Print quality control graphs for each dataset before removing contaminated data +# and before adjusting missing data to max theoretical values -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 plot +# Plate analysis is a quality check to identify plate effects containing anomalies -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_L <- + ggplot(X, aes(Scan, l, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary(fun = mean, geom = "point", size = 0.6) + + ggtitle("Plate analysis by Drug Conc for L 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_K <- + ggplot(X, aes(Scan, K, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary(fun = mean, geom = "point", size = 0.6) + + ggtitle("Plate analysis by Drug Conc for K 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_r <- + ggplot(X, aes(Scan, r, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary(fun = 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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary(fun = 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_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_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_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() -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 +# 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$X1stBackgrd - -#raw l vs K before QC -Raw_l_vs_K_beforeQC <- ggplot(X,aes(l,K,color=as.factor(Conc_Num))) + geom_point(aes(ORF=ORF,Gene=Gene,Delta_Backgrd=Delta_Backgrd),shape=3) + +# Raw l vs K before QC +Raw_l_vs_K_beforeQC <- + ggplot(X, aes(l, K, color = as.factor(Conc_Num))) + + geom_point(aes(ORF = ORF, Gene = Gene, Delta_Backgrd = Delta_Backgrd), shape = 3) + ggtitle("Raw L vs K before QC") + - theme_Publication_legend_right() -pdf(paste(outDir_QC,"Raw_L_vs_K_beforeQC.pdf",sep=""),width = 12,height = 8) + theme_publication_legend_right() + +pdf(paste(outDir_QC, "Raw_L_vs_K_beforeQC.pdf", sep = ""), width = 12, height = 8) Raw_l_vs_K_beforeQC + dev.off() + pgg <- ggplotly(Raw_l_vs_K_beforeQC) -#pgg -plotly_path <- paste(outDir_QC,"Raw_L_vs_K_beforeQC.html",sep="") -saveWidget(pgg, file=plotly_path, selfcontained =TRUE) +plotly_path <- paste(outDir_QC, "Raw_L_vs_K_beforeQC.html", sep = "") +saveWidget(pgg, file = plotly_path, selfcontained = TRUE) +# Set delta background tolerance based on 3 sds from the mean delta background +Delta_Background_Tolerance <- mean(X$Delta_Backgrd) + (delBGFactor * sd(X$Delta_Backgrd)) +# Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd)) +print(paste("Delta_Background_Tolerance is", Delta_Background_Tolerance, sep = " ")) -#set delta background tolerance based on 3 sds from the mean delta background -Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(delBGFactor*sd(X$Delta_Backgrd)) -#Delta_Background_Tolerance <- mean(X$Delta_Backgrd)+(3*sd(X$Delta_Backgrd)) -print(paste("Delta_Background_Tolerance is",Delta_Background_Tolerance,sep=" ")) +Plate_Analysis_Delta_Backgrd <- + ggplot(X, aes(Scan, Delta_Backgrd, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2, position = "jitter") + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary(fun = mean, geom = "point", size = 0.6) + + ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + + theme_publication() -Plate_Analysis_Delta_Backgrd <- ggplot(X,aes(Scan,Delta_Backgrd,color=as.factor(Conc_Num))) + geom_point(shape=3,size=0.2,position="jitter") + - stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), - geom = "errorbar") + stat_summary(fun.y = mean, geom = "point",size=0.6) + - ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication() +Plate_Analysis_Delta_Backgrd_Box <- + ggplot(X, aes(as.factor(Scan), Delta_Backgrd, color = as.factor(Conc_Num))) + + geom_boxplot() + + ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + + theme_publication() -Plate_Analysis_Delta_Backgrd_Box <- ggplot(X,aes(as.factor(Scan),Delta_Backgrd,color=as.factor(Conc_Num))) + geom_boxplot() + - ggtitle("Plate analysis by Drug Conc for Delta_Backgrd before quality control") + theme_Publication() +X_Delta_Backgrd_above_Tolerance <- + X[X$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() - -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(outDir_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf",sep=""),width = 12,height = 8) +pdf(paste(outDir_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(outDir_QC,"Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html",sep="") -saveWidget(pgg, file=plotly_path, selfcontained =TRUE) +plotly_path <- paste(outDir_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() +# 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() +# 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(outDir_QC,"Frequency_Delta_Background.pdf",sep=""),width = 12,height = 8) +pdf(file = paste(outDir_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" +# 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),]$NG <- 1) +try(X[X$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" +# 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,]$DB <- 1) +try(X[X$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,]$K <- NA) +# Replace the CPPs for l, r, AUC and K (must be last!) for removed data +try(X[X$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) +# QC Plots +Plate_Analysis_L_afterQC <- ggplot(X, aes(Scan, l, color = as.factor(Conc_Num))) + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = mean, + geom = "point", + size = 0.6 + ) + + ggtitle("Plate analysis by Drug Conc for L after quality control") + theme_publication() -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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = mean, + geom = "point", + size = 0.6 + ) + + ggtitle("Plate analysis by Drug Conc for K 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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = mean, + geom = "point", + size = 0.6 + ) + + ggtitle("Plate analysis by Drug Conc for r 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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = mean, + geom = "point", + size = 0.6 + ) + + ggtitle("Plate analysis by Drug Conc for AUC 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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = mean, + geom = "point", + size = 0.6 + ) + + ggtitle("Plate analysis by Drug Conc for Delta_Backgrd 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_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_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_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_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_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_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() -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(outDir_QC,"Plate_Analysis.pdf",sep=""),width = 14,height=9) +# Print the plate analysis data before and after QC +pdf(file = paste(outDir_QC, "Plate_Analysis.pdf", sep = ""), width = 14, height = 9) Plate_Analysis_L Plate_Analysis_L_afterQC Plate_Analysis_K @@ -451,8 +581,8 @@ Plate_Analysis_Delta_Backgrd Plate_Analysis_Delta_Backgrd_afterQC dev.off() -#print the plate analysis data before and after QC -pdf(file=paste(outDir_QC,"Plate_Analysis_Boxplots.pdf",sep=""),width = 18,height=9) +# Print the plate analysis data before and after QC +pdf(file = paste(outDir_QC, "Plate_Analysis_Boxplots.pdf", sep = ""), width = 18, height = 9) Plate_Analysis_L_Box Plate_Analysis_L_Box_afterQC Plate_Analysis_K_Box @@ -465,51 +595,125 @@ 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() +# 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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = 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_K_afterQC_Z <- + ggplot(X_noZero, aes(Scan, K, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = 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_r_afterQC_Z <- + ggplot(X_noZero, aes(Scan, r, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = 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_AUC_afterQC_Z <- + ggplot(X_noZero, aes(Scan, AUC, color = as.factor(Conc_Num))) + + geom_point(shape = 3, size = 0.2) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = 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_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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar" + ) + + stat_summary( + fun = 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_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_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_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_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() -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(outDir_QC,"Plate_Analysis_noZeros.pdf",sep=""),width = 14,height=9) +# Print the plate analysis data before and after QC +pdf(file = paste(outDir_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 @@ -517,8 +721,8 @@ 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(outDir_QC,"Plate_Analysis_noZeros_Boxplots.pdf",sep=""),width = 18,height=9) +# Print the plate analysis data before and after QC +pdf(file = paste(outDir_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 @@ -526,329 +730,385 @@ Plate_Analysis_AUC_Box_afterQC_Z Plate_Analysis_Delta_Backgrd_Box_afterQC_Z dev.off() -#remove dataset with zeros removed +# Remove dataset with zeros removed rm(X_noZero) +# X_test_missing_and_removed <- X[X$Removed == 1, ] -#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) +# 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(outDir,"SummaryStats_ALLSTRAINS.csv"),row.names = FALSE) -#+++++END QC SECTION+++++++++++++++++++++++++++++++++++++++++++++++++++++++ +# print(X_stats_ALL_L) +write.csv(X_stats_ALL, file = paste(outDir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE) -##### Part 3 - Generate summary statistics and calculate the max theoretical L value -##### Calculate the Z score at each drug conc for each deletion strain +# 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 +# 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,]$AUC <- NA +# First part of loop will go through for each background strain +# In most cases there will only be one YDL227C +for (s in Background_Strains) { + X_Background <- X[X$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) + + 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) + + 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$sd) - - X_stats_BY_r <- ddply(X_Background, c("OrfRep","Conc_Num","Conc_Num_Factor"), summarise, - N = length(r), - mean = mean(r,na.rm=TRUE), - median = median(r,na.rm=TRUE), - max = max(r,na.rm=TRUE), - min = min(r,na.rm=TRUE), - sd = sd(r,na.rm=TRUE), - se = sd / sqrt(N-1) + X_stats_BY_r <- ddply( + X_Background, + c("OrfRep", "Conc_Num", "Conc_Num_Factor"), + summarise, + N = length(r), + mean = mean(r, na.rm = TRUE), + median = median(r, na.rm = TRUE), + max = max(r, na.rm = TRUE), + min = min(r, na.rm = TRUE), + sd = sd(r, na.rm = TRUE), + se = sd / sqrt(N - 1) ) X1_SD_r <- max(X_stats_BY_r$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) + 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) + 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(outDir,"SummaryStats_BackgroundStrains.csv"),row.names=FALSE) + write.csv(X_stats_BY, file = paste(outDir, "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$sd[1])) ,] - + # Calculate the max theoretical L values + # Only look for max values when K is within 2SD of the ref strain + for (q in unique(X$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) + 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$sd[1])) , ] + X_outside_2SD_K <- + rbind(X_outside_2SD_K, X_outside_2SD_K_temp) } } - - X_stats_BY_L_within_2SD_K <- ddply(X_within_2SD_K, c("Conc_Num","Conc_Num_Factor"), summarise, - N = (length(l)), - mean = mean(l), - median = median(l), - max = max(l,na.rm=TRUE), - min = min(l,na.rm=TRUE), - sd = sd(l), - se = sd / sqrt(N-1), - z_max = (max-mean)/sd + + X_stats_BY_L_within_2SD_K <- ddply( + X_within_2SD_K, + c("Conc_Num", "Conc_Num_Factor"), + summarise, + N = (length(l)), + mean = mean(l), + median = median(l), + max = max(l, na.rm = TRUE), + min = min(l, na.rm = TRUE), + sd = sd(l), + se = sd / sqrt(N - 1), + z_max = (max - mean) / sd ) + print(X_stats_BY_L_within_2SD_K) + X1_SD_within_2SD_K <- max(X_stats_BY_L_within_2SD_K$sd) - write.csv(X_stats_BY_L_within_2SD_K,file=paste(outDir_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) + + write.csv( + X_stats_BY_L_within_2SD_K, + file = paste(outDir_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$l, ] + Outside_2SD_K_L_vs_K <- + ggplot(X_outside_2SD_K, aes(l, K, color = as.factor(Conc_Num))) + + geom_point(aes(ORF = ORF, Gene = Gene, Delta_Backgrd = Delta_Backgrd), shape = 3) + + ggtitle("Raw L vs K for strains falling outside 2SD of the K mean at each conc") + + theme_publication_legend_right() - #X1_SD_outside_2SD_K <- X[X$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(outDir_QC,"Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8) + pdf(paste(outDir_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(outDir_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(outDir_QC,"DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf",sep=""),width = 10,height = 8) + plotly_path <- paste(outDir_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(outDir_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(outDir_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 + + # pgg + plotly_path <- paste(outDir_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$mean[1] - - #create empty plots for plotting element - p_l <- ggplot() - p_K <- ggplot() - p_r <- ggplot() - p_AUC <- ggplot() - - p_rf_l <- ggplot() - p_rf_K <- ggplot() - p_rf_r <- ggplot() - p_rf_AUC <- ggplot() - - #get only the deletion strains + + # Create empty plots for plotting element + p_l <- ggplot() + p_K <- ggplot() + p_r <- ggplot() + p_AUC <- ggplot() + + p_rf_l <- ggplot() + p_rf_K <- ggplot() + p_rf_r <- ggplot() + p_rf_AUC <- ggplot() + + # Get only the deletion strains X2 <- X - X2 <- X2[X2$OrfRep != "YDL227C",] - - #if set to max theoretical value, add a 1 to SM, if not, leave as 0 - #SM = Set to Max + X2 <- 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$Conc_Num))){ + # Set the missing values to the highest theoretical value at each drug conc for L + # Leave other values as 0 for the max/min + for (i in 1:length(unique(X2$Conc_Num))) { Concentration <- unique(X2$Conc_Num)[i] - X2_temp <- X2[X2$Conc_Num == Concentration,] - if(Concentration == 0){ + X2_temp <- X2[X2$Conc_Num == Concentration, ] + if (Concentration == 0) { X2_new <- X2_temp - print(paste("Check loop order, conc =",Concentration,sep=" ")) + print(paste("Check loop order, conc =", Concentration, sep = " ")) } - if(Concentration > 0){ - try(X2_temp[X2_temp$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$max[i] - print(paste("Check loop order, conc =",Concentration,sep=" ")) - - X2_new <- rbind(X2_new,X2_temp) - + if (Concentration > 0) { + try(X2_temp[X2_temp$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$max[i] + print(paste("Check loop order, conc =", Concentration, sep = " ")) + X2_new <- rbind(X2_new, X2_temp) } } + X2 <- X2_new - - - #get only the RF strains + + # Get only the RF strains X2_RF <- X - X2_RF <- X2_RF[X2_RF$OrfRep == "YDL227C",] - #if set to max theoretical value, add a 1 to SM, if not, leave as 0 - #SM = Set to Max + X2_RF <- 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$Conc_Num))){ + # Set the missing values to the highest theoretical value at each drug conc for L + # Leave other values as 0 for the max/min + for (i in 1:length(unique(X2_RF$Conc_Num))) { Concentration <- unique(X2_RF$Conc_Num)[i] - X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration,] - if(Concentration == 0){ + X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration, ] + if (Concentration == 0) { X2_RF_new <- X2_RF_temp - print(paste("Check loop order, conc =",Concentration,sep=" ")) + print(paste("Check loop order, conc =", Concentration, sep = " ")) } - if(Concentration > 0){ - try(X2_RF_temp[X2_RF_temp$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) - + 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$max[i]) + print(paste("If error, refs have no L values outside theoretical max L, for REFs, conc =", Concentration, sep = " ")) + X2_RF_new <- rbind(X2_RF_new, X2_RF_temp) } } X2_RF <- X2_RF_new - - - #######Part 4 Get the RF Z score values - #Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score - X2_RF$OrfRep <- paste(X2_RF$OrfRep,X2_RF$Gene,X2_RF$Num.,sep="_") - + + # Part 4 Get the RF Z score values + # Change the OrfRep Column to include the RF strain, the Gene name and the Num. so each RF gets its own score + X2_RF$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 + # 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$Gene <- unique(X2$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$Z_lm_L <- NA + InteractionScores_RF$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$Z_lm_K <- NA + InteractionScores_RF$Z_lm_K <- NA InteractionScores_RF$R_Squared_K <- NA InteractionScores_RF$Sum_Z_Score_K <- NA InteractionScores_RF$Avg_Zscore_K <- NA @@ -868,44 +1128,45 @@ for(s in Background_Strains){ InteractionScores_RF$Avg_Zscore_AUC <- NA InteractionScores_RF$NG <- NA InteractionScores_RF$SM <- NA - - - for(i in 1:num_genes_RF){ - #get each deletion strain ORF + + 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) + # 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$mean_L[1] == 0 ){ + + # Get shift vals + # if L is 0, that means the no growth on no drug + # if L is NA at 0, that means the spot was removed due to contamination + # if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift + # otherwise calculate shift at no drug conc + if (is.na(X_stats_interaction$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 @@ -914,169 +1175,199 @@ for(s in Background_Strains){ X_stats_interaction$Z_Shift_K <- 0 X_stats_interaction$Z_Shift_r <- 0 X_stats_interaction$Z_Shift_AUC <- 0 - }else{ + } else { X_stats_interaction$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$sd[1] + X_stats_interaction$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$sd[1] } - - - #get WT vals + + # Get WT vals X_stats_interaction$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$mean - - #Get WT SD + + # Get WT SD X_stats_interaction$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$mean_L[1])){ - - #calculate expected values + + # Only get scores if there's growth at no drug + if (X_stats_interaction$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 + + # 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_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 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 - + # 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, ]$WT_AU } - - - - #calculate Z score at each concentration - X_stats_interaction$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$coefficients[1] + + # Calculate Z score at each concentration + X_stats_interaction$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] + 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$coefficients[1] + gene_interaction_r <- MAX_CONC * (gene_lm_r$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] + 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) + + # 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$mean_L[1]) ){ - - #calculate expected values + + if (X_stats_interaction$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 + + # 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_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 missing 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 + # 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$WT_sd_AUC) - #calculate Z score at each concentration - X_stats_interaction$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$WT_sd_AUC) - - #NA values for the next part since there's an NA or 0 at the no drug. + # NA values for the next part since there's an NA or 0 at the no drug. gene_lm_L <- NA gene_lm_K <- NA gene_lm_r <- NA gene_lm_AUC <- NA - gene_interaction_L <- NA r_squared_l <- NA gene_interaction_K <- NA @@ -1085,41 +1376,34 @@ for(s in Background_Strains){ r_squared_r <- NA gene_interaction_AUC <- NA r_squared_AUC <- NA - X_stats_interaction$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$OrfRep == Gene_Sel] <- gene_interaction_AUC @@ -1128,132 +1412,153 @@ for(s in Background_Strains){ InteractionScores_RF$Avg_Zscore_AUC[InteractionScores_RF$OrfRep == Gene_Sel] <- NA } - if(i == 1){ + if (i == 1) { X_stats_interaction_ALL_RF <- X_stats_interaction } - if(i > 1){ - X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF,X_stats_interaction) + + if (i > 1) { + X_stats_interaction_ALL_RF <- rbind(X_stats_interaction_ALL_RF, X_stats_interaction) } - InteractionScores_RF$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) - + 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$lm_Score_AUC,na.rm=TRUE) + lm_sd_L <- sd(InteractionScores_RF$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$lm_Score_AUC, na.rm = TRUE) - lm_mean_L <- mean(InteractionScores_RF$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$lm_Score_AUC,na.rm=TRUE) + print(paste("Mean RF linear regression score L", lm_mean_L)) - print(paste("Mean RF linear regression score L",lm_mean_L)) + InteractionScores_RF$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$NG, decreasing = TRUE), ] + write.csv(InteractionScores_RF, paste(outDir, "RF_ZScores_Interaction.csv", sep = ""), row.names = FALSE) - - InteractionScores_RF$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$NG,decreasing=TRUE),] - write.csv(InteractionScores_RF,paste(outDir,"RF_ZScores_Interaction.csv",sep=""),row.names=FALSE) - - - for(i in 1:num_genes_RF){ + for (i in 1:num_genes_RF) { Gene_Sel <- unique(InteractionScores_RF$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,] + 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$Conc_Num)) + ) + + scale_y_continuous(breaks = c(-60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60)) + + theme_publication() - p_rf_l[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_L)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) + - coord_cartesian(ylim = c(-65,65)) + - geom_errorbar(aes(ymin=0-(2*WT_sd_l), ymax=0+(2*WT_sd_l)),alpha=0.3) + - ggtitle(paste(X_ZCalculations$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$Conc_Num)) + ) + + scale_y_continuous(breaks = c(-60, -50, -40, -30, -20, -10, 0, 10, 20, 30, 40, 50, 60)) + + theme_publication() - p_rf_K[[i]] <- ggplot(X_ZCalculations,aes(Conc_Num_Factor,Delta_K)) + geom_point() + geom_smooth(method="lm",formula=y~x,se=FALSE) + - coord_cartesian(ylim = c(-65,65)) + - geom_errorbar(aes(ymin=0-(2*WT_sd_K), ymax=0+(2*WT_sd_K)),alpha=0.3) + - ggtitle(paste(X_ZCalculations$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() - 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){ + 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) + 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(outDir,"RF_ZScore_Calculations.csv",sep=""),row.names = FALSE) + write.csv(X_stats_interaction_ALL_RF_final, paste(outDir, "RF_ZScore_Calculations.csv", sep = ""), row.names = FALSE) + # Part 5 - Get Zscores for Gene deletion strains - ####### Part 5 - Get Zscores for Gene deletion strains - - - - #get total number of genes for the next loop + # Get total number of genes for the next loop num_genes <- length(unique(X2$OrfRep)) - print(num_genes) + # print(num_genes) - #create the output data.frame containing columns for each deletion strain + # Create the output data.frame containing columns for each deletion strain InteractionScores <- unique(X2["OrfRep"]) - #InteractionScores$Gene <- unique(X2$Gene) + # InteractionScores$Gene <- unique(X2$Gene) InteractionScores$Gene <- NA InteractionScores$Raw_Shift_L <- NA InteractionScores$Z_Shift_L <- NA @@ -1287,42 +1592,44 @@ for(s in Background_Strains){ InteractionScores$DB <- NA InteractionScores$SM <- NA - for(i in 1:num_genes){ - #get each deletion strain ORF + 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,] + # Extract only the current deletion strain and its data + X_Gene_Sel <- X2[X2$OrfRep == Gene_Sel, ] - X_stats_interaction <- ddply(X_Gene_Sel, c("OrfRep","Gene","Conc_Num","Conc_Num_Factor"), summarise, - N = (length(l)), - mean_L = mean(l,na.rm = TRUE), - median_L = median(l,na.rm = TRUE), - sd_L = sd(l,na.rm = TRUE), - se_L = sd_L / sqrt(N-1), - mean_K = mean(K,na.rm = TRUE), - median_K = median(K,na.rm = TRUE), - sd_K = sd(K,na.rm = TRUE), - se_K = sd_K / sqrt(N-1), - mean_r = mean(r,na.rm = TRUE), - median_r = median(r,na.rm = TRUE), - sd_r = sd(r,na.rm = TRUE), - se_r = sd_r / sqrt(N-1), - mean_AUC = mean(AUC,na.rm = TRUE), - median_AUC = median(AUC,na.rm = TRUE), - sd_AUC = sd(AUC,na.rm = TRUE), - se_AUC = sd_AUC / sqrt(N-1), - NG = sum(NG,na.rm=TRUE), - DB= sum(DB,na.rm=TRUE), - SM = sum(SM,na.rm=TRUE) + X_stats_interaction <- ddply( + X_Gene_Sel, + c("OrfRep", "Gene", "Conc_Num", "Conc_Num_Factor"), + summarise, + N = (length(l)), + mean_L = mean(l, na.rm = TRUE), + median_L = median(l, na.rm = TRUE), + sd_L = sd(l, na.rm = TRUE), + se_L = sd_L / sqrt(N - 1), + mean_K = mean(K, na.rm = TRUE), + median_K = median(K, na.rm = TRUE), + sd_K = sd(K, na.rm = TRUE), + se_K = sd_K / sqrt(N - 1), + mean_r = mean(r, na.rm = TRUE), + median_r = median(r, na.rm = TRUE), + sd_r = sd(r, na.rm = TRUE), + se_r = sd_r / sqrt(N - 1), + mean_AUC = mean(AUC, na.rm = TRUE), + median_AUC = median(AUC, na.rm = TRUE), + sd_AUC = sd(AUC, na.rm = TRUE), + se_AUC = sd_AUC / sqrt(N - 1), + NG = sum(NG, na.rm = TRUE), + DB = sum(DB, na.rm = TRUE), + SM = sum(SM, na.rm = TRUE) ) - - #Get shift vals - #if L is 0, that means the no growth on no drug - #if L is NA at 0, that means the spot was removed due to contamination - #if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift - #otherwise calculate shift at no drug conc - if(is.na(X_stats_interaction$mean_L[1]) | X_stats_interaction$mean_L[1] == 0 ){ + # Get shift vals + # if L is 0, that means the no growth on no drug + # if L is NA at 0, that means the spot was removed due to contamination + # if L is 0, keep the shift at 0 and for other drug concs calculate delta Ls with no shift + # otherwise calculate shift at no drug conc + if (is.na(X_stats_interaction$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 @@ -1331,192 +1638,223 @@ for(s in Background_Strains){ X_stats_interaction$Z_Shift_K <- 0 X_stats_interaction$Z_Shift_r <- 0 X_stats_interaction$Z_Shift_AUC <- 0 - }else{ + } else { X_stats_interaction$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$sd[1] + X_stats_interaction$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$sd[1] } - - #get WT vals + # Get WT vals X_stats_interaction$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$mean - #Get WT SD + # Get WT SD X_stats_interaction$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$mean_L[1])){ + # Only get scores if there's growth at no drug + if (X_stats_interaction$mean_L[1] != 0 && !is.na(X_stats_interaction$mean_L[1])) { - #calculate expected values + # Calculate expected values X_stats_interaction$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 + # 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_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 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 - + + # 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$WT_sd_AUC) + # Get linear model + gene_lm_L <- lm(formula = Delta_L ~ Conc_Num_Factor, data = X_stats_interaction) + gene_lm_K <- lm(formula = Delta_K ~ Conc_Num_Factor, data = X_stats_interaction) + gene_lm_r <- lm(formula = Delta_r ~ Conc_Num_Factor, data = X_stats_interaction) + gene_lm_AUC <- lm(formula = Delta_AUC ~ Conc_Num_Factor, data = X_stats_interaction) - #calculate Z score at each concentration - X_stats_interaction$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$coefficients[1] + # Get interaction score calculated by linear model and R-squared value for the fit + gene_interaction_L <- MAX_CONC * (gene_lm_L$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] + 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$coefficients[1] + gene_interaction_r <- MAX_CONC * (gene_lm_r$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] + 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 + # 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$Zscore_AUC,na.rm = TRUE)/(Total_Conc_Nums-1) + # Report the scores + InteractionScores$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$mean_L[1]) ){ + if (X_stats_interaction$mean_L[1] == 0 || is.na(X_stats_interaction$mean_L[1])) { - #calculate expected values + # Calculate expected values X_stats_interaction$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 + # 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_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 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 + + # 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$WT_sd_AUC) - #calculate Z score at each concentration - X_stats_interaction$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$WT_sd_AUC) - - #NA values for the next part since there's an NA or 0 at the no drug. + # NA values for the next part since there's an NA or 0 at the no drug. gene_lm_L <- NA gene_lm_K <- NA gene_lm_r <- NA - gene_interaction_L <- NA r_squared_l <- NA gene_interaction_K <- NA r_squared_K <- NA gene_interaction_r <- NA r_squared_r <- NA - X_stats_interaction$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$OrfRep == Gene_Sel] <- NA @@ -1524,15 +1862,13 @@ for(s in Background_Strains){ InteractionScores$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$OrfRep == Gene_Sel] <-NA + InteractionScores$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$OrfRep == Gene_Sel] <- NA @@ -1540,7 +1876,6 @@ for(s in Background_Strains){ InteractionScores$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$OrfRep == Gene_Sel] <- NA @@ -1550,488 +1885,718 @@ for(s in Background_Strains){ InteractionScores$Avg_Zscore_AUC[InteractionScores$OrfRep == Gene_Sel] <- NA } - if(i == 1){ + if (i == 1) { X_stats_interaction_ALL <- X_stats_interaction } - if(i > 1){ - X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL,X_stats_interaction) + if (i > 1) { + X_stats_interaction_ALL <- rbind(X_stats_interaction_ALL, X_stats_interaction) } - InteractionScores$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) - + 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$NG,decreasing=TRUE),] + InteractionScores <- InteractionScores[order(InteractionScores$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$NG,decreasing=TRUE),] - write.csv(InteractionScores,paste(outDir,"ZScores_Interaction.csv",sep=""),row.names=FALSE) + # X_stats_interaction_ALL <- X_stats_interaction_ALL[order(X_stats_interaction_ALL$NG, decreasing = TRUE), ] + write.csv(InteractionScores, paste(outDir, "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[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(outDir, "ZScores_Interaction_DeletionEnhancers_L.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_K, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_K.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_suppressors_L, + paste(outDir, "ZScores_Interaction_DeletionSuppressors_L.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_suppressors_K, + paste(outDir, "ZScores_Interaction_DeletionSuppressors_K.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L, + paste(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L, + paste(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K, + paste(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K, + paste(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv", sep = ""), row.names = FALSE) - 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),] + # 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,paste(outDir,"ZScores_Interaction_DeletionEnhancers_L.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_K,paste(outDir,"ZScores_Interaction_DeletionEnhancers_K.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_suppressors_L,paste(outDir,"ZScores_Interaction_DeletionSuppressors_L.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_suppressors_K,paste(outDir,"ZScores_Interaction_DeletionSuppressors_K.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,paste(outDir,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,paste(outDir,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_L,paste(outDir,"ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_L,paste(outDir,"ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_lm_Suppressors_AvgZscore_K,paste(outDir,"ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K,paste(outDir,"ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv",sep=""),row.names=FALSE) + write.csv(InteractionScores_deletion_enhancers_L_lm, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_L_lm.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_K_lm, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_K_lm.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_suppressors_L_lm, + paste(outDir, "ZScores_Interaction_DeletionSuppressors_L_lm.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_suppressors_K_lm, + paste(outDir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv", sep = ""), row.names = FALSE) + write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm, + paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv", sep = ""), row.names = FALSE) + write.csv(Labels, file = paste(studyInfo), row.names = FALSE) + # write.table(Labels, file = paste("../Code/StudyInfo.txt"), sep = "\t", 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(outDir,"ZScores_Interaction_DeletionEnhancers_L_lm.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_K_lm,paste(outDir,"ZScores_Interaction_DeletionEnhancers_K_lm.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_suppressors_L_lm,paste(outDir,"ZScores_Interaction_DeletionSuppressors_L_lm.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_suppressors_K_lm,paste(outDir,"ZScores_Interaction_DeletionSuppressors_K_lm.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L_lm,paste(outDir,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv",sep=""),row.names=FALSE) - write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K_lm,paste(outDir,"ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv",sep=""),row.names=FALSE) - - - write.csv(Labels,file=paste(StudyInfo),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){ + 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$OrfRep == Gene_Sel,] + X_ZCalculations <- X_stats_interaction_ALL[X_stats_interaction_ALL$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_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_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_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() - 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){ + 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) + 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(outDir,"ZScore_Calculations.csv",sep=""),row.names = FALSE) - - - - - + write.csv(X_stats_interaction_ALL_final, paste(outDir, "ZScore_Calculations.csv", sep = ""), row.names = FALSE) Blank <- ggplot(X2_RF) + geom_blank() - pdf(paste(outDir,"InteractionPlots.pdf",sep=""),width = 16, height = 16, onefile = TRUE) + pdf(paste(outDir, "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) + 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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red") + + stat_summary(fun = 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() - 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$SM) + - theme_Publication() + K_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, K)) + geom_point(position = "jitter", size = 1) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red") + + stat_summary(fun = mean, geom = "point", color = "red") + + scale_x_continuous( + name = unique(X$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() - 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$SM) + - theme_Publication() + R_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, r)) + geom_point(position = "jitter", size = 1) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red") + + stat_summary(fun = mean, geom = "point", color = "red") + + scale_x_continuous( + name = unique(X$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() - 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$SM) + - theme_Publication() + AUC_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, AUC)) + geom_point(position = "jitter", size = 1) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red") + + stat_summary(fun = mean, geom = "point", color = "red") + + scale_x_continuous( + name = unique(X$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$Conc_Num)) + ) + + ggtitle(paste(s, "Scatter RF for L with SD", sep = " ")) + + coord_cartesian(ylim = c(0, 160)) + + theme_publication() - AUC_Stats <- ggplot(X2_RF,aes(Conc_Num_Factor,AUC)) + geom_point(position="jitter",size=1) + - stat_summary(fun.y = mean, fun.ymin = function(x) mean(x) - sd(x), fun.ymax = function(x) mean(x) + sd(x), - geom = "errorbar",color="red") + stat_summary(fun.y = mean, geom = "point",color="red") + - scale_x_continuous(name = unique(X$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() + 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() - 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$Conc_Num))) + - ggtitle(paste(s,"Scatter RF for L with SD", sep = " ")) + coord_cartesian(ylim=c(0,160)) + - theme_Publication() + AUC_Stats_Box <- ggplot(X2_RF, aes(as.factor(Conc_Num_Factor), AUC)) + + geom_boxplot() + + scale_x_discrete( + name = unique(X$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() - 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$Conc_Num))) + - ggtitle(paste(s,"Scatter RF for K with SD", sep = " ")) + coord_cartesian(ylim=c(0,130)) + - theme_Publication() + grid.arrange(L_Stats, K_Stats, R_Stats, AUC_Stats, ncol = 2, nrow = 2) + grid.arrange(L_Stats_Box, K_Stats_Box, r_Stats_Box, AUC_Stats_Box, ncol = 2, nrow = 2) - r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() + - scale_x_discrete(name = unique(X$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() + # 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) - 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 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 + + # Loop for printing each plot num <- 0 - for(m in 1:(round((num_genes)/3)-1)){ + 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]] + 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 (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 == 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 == 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 == 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) + 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(outDir,"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) + + pdf(paste(outDir, "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 = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red" + ) + + stat_summary(fun = 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() - 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$SM) + - theme_Publication() + K_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, K)) + + geom_point(position = "jitter", size = 1) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red" + ) + + stat_summary(fun = mean, geom = "point", color = "red") + + scale_x_continuous( + name = unique(X$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() - 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$SM) + - theme_Publication() + R_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, r)) + + geom_point(position = "jitter", size = 1) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red" + ) + + stat_summary(fun = mean, geom = "point", color = "red") + + scale_x_continuous( + name = unique(X$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() - 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$SM) + - theme_Publication() + AUC_Stats <- ggplot(X2_RF, aes(Conc_Num_Factor, AUC)) + + geom_point(position = "jitter", size = 1) + + stat_summary( + fun = mean, + fun.min = function(x) mean(x) - sd(x), + fun.max = function(x) mean(x) + sd(x), + geom = "errorbar", color = "red" + ) + + stat_summary(fun = mean, geom = "point", color = "red") + + scale_x_continuous( + name = unique(X$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() - 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$Conc_Num)) + ) + + ggtitle(paste(s, "Scatter RF for K with SD", sep = " ")) + + coord_cartesian(ylim = c(0, 160)) + + theme_publication() - L_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),l)) + geom_boxplot() + - scale_x_discrete(name = unique(X$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() + 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$Conc_Num)) + ) + + ggtitle(paste(s, "Scatter RF for r with SD", sep = " ")) + + coord_cartesian(ylim = c(0, 1)) + + theme_publication() - K_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),K)) + geom_boxplot() + - scale_x_discrete(name = unique(X$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() + 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$Conc_Num)) + ) + + ggtitle(paste(s, "Scatter RF for AUC with SD", sep = " ")) + coord_cartesian(ylim = c(12000, 0)) + + theme_publication() - r_Stats_Box <- ggplot(X2_RF,aes(as.factor(Conc_Num_Factor),r)) + geom_boxplot() + - scale_x_discrete(name = unique(X$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() + 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) - 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() + # 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) - - 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 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 + + # Loop for printing each plot num <- 0 - for(m in 1:(round((num_genes_RF)/3)-1)){ + 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]] + 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 (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 == 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 == 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 == 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) + 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 - - + # 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),]$Avg_Zscore_AUC <- 0.001 + InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$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$K_Rank <- NA @@ -2043,11 +2608,10 @@ for(s in Background_Strains){ InteractionScores_AdjustMissing$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),]$Z_lm_AUC <- 0.001 + InteractionScores_AdjustMissing[is.na(InteractionScores_AdjustMissing$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$K_Rank_lm <- NA @@ -2059,661 +2623,864 @@ for(s in Background_Strains){ InteractionScores_AdjustMissing$r_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_r) InteractionScores_AdjustMissing$AUC_Rank_lm <- rank(InteractionScores_AdjustMissing$Z_lm_AUC) + # Rank plots + Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) + + ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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$Avg_Zscore_L <= -2, ])[1])) + + theme_publication() - Rank_L_1SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + - ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 1,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -1,])[1])) + - theme_Publication() + Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) + + ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Enhancers =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3, ])[1])) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Suppressors =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3, ])[1])) + + theme_publication() - Rank_L_2SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + - ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 2,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -2,])[1])) + - theme_Publication() + Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) + + ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1, ])[1])) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1, ])[1])) + + theme_publication() - Rank_L_3SD <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank,Avg_Zscore_L)) + - ggtitle("Average Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Avg Z score L") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L >= 3,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_L <= -3,])[1])) + - theme_Publication() + Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank, Avg_Zscore_K)) + + ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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$Avg_Zscore_K >= 3, ])[1])) + + theme_publication() - Rank_K_1SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + - ggtitle("Average Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Avg Z score K") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -1,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 1,])[1])) + - theme_Publication() + Rank_L_1SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) + + ggtitle("Average Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Avg Z score L") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) + + theme_publication() - Rank_K_2SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + - ggtitle("Average Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Avg Z score K") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K <= -2,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Avg_Zscore_K >= 2,])[1])) + - theme_Publication() + Rank_L_2SD_notext <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank, Avg_Zscore_L)) + + ggtitle("Average Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Avg Z score L") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (2), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-2), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-2, 2)) + geom_point(size = 0.1, shape = 3) + + theme_publication() - Rank_K_3SD <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank,Avg_Zscore_K)) + - ggtitle("Average Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Avg Z score K") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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_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_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_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_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_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() - 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() + pdf(paste(outDir, "RankPlots.pdf", sep = ""), width = 18, height = 12, onefile = TRUE) - - 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(outDir,"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) + 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$Z_lm_L <= -1, ])[1])) + + theme_publication() - Rank_L_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + - ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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$Z_lm_L <= -2, ])[1])) + + theme_publication() - Rank_L_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + - ggtitle("Interaction Z score vs. Rank for L above 2SD") + xlab("Rank") + ylab("Int Z score L") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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$Z_lm_L <= -3, ])[1])) + + theme_publication() - Rank_L_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(L_Rank_lm,Z_lm_L)) + - ggtitle("Interaction Z score vs. Rank for L above 3SD") + xlab("Rank") + ylab("Int Z score L") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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$Z_lm_K >= 2, ])[1])) + + theme_publication() - Rank_K_1SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + - ggtitle("Interaction Z score vs. Rank for K above 1SD") + xlab("Rank") + ylab("Int Z score K") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (1),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-1),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-1,1)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -1,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 1,])[1])) + - theme_Publication() + Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing, aes(K_Rank_lm, Z_lm_K)) + + ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (3), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-3), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-3, 3)) + geom_point(size = 0.1, shape = 3) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = -10, label = paste("Deletion Enhancers =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -3, ])[1])) + + annotate("text", x = (dim(InteractionScores_AdjustMissing)[1] / 2), y = 10, label = paste("Deletion Suppressors =", + dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 3, ])[1])) + + theme_publication() - Rank_K_2SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + - ggtitle("Interaction Z score vs. Rank for K above 2SD") + xlab("Rank") + ylab("Int Z score K") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (2),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-2),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-2,2)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K <= -2,])[1])) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=10,label=paste("Deletion Suppressors =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$Z_lm_K >= 2,])[1])) + - theme_Publication() + Rank_L_1SD_notext_lm <- ggplot(InteractionScores_AdjustMissing, aes(L_Rank_lm, Z_lm_L)) + + ggtitle("Interaction Z score vs. Rank for L above 1SD") + xlab("Rank") + ylab("Int Z score L") + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (1), ymax = Inf, fill = "#542788", alpha = 0.3) + + annotate("rect", xmin = -Inf, xmax = Inf, ymin = (-1), ymax = -Inf, fill = "orange", alpha = 0.3) + + geom_hline(yintercept = c(-1, 1)) + geom_point(size = 0.1, shape = 3) + + theme_publication() - Rank_K_3SD_lm <- ggplot(InteractionScores_AdjustMissing,aes(K_Rank_lm,Z_lm_K)) + - ggtitle("Interaction Z score vs. Rank for K above 3SD") + xlab("Rank") + ylab("Int Z score K") + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (3),ymax = Inf,fill="#542788", alpha=0.3) + - annotate("rect",xmin = -Inf, xmax = Inf, ymin = (-3),ymax = -Inf,fill="orange", alpha=0.3) + - geom_hline(yintercept=c(-3,3)) + geom_point(size=0.1,shape=3) + - annotate("text",x=(dim(InteractionScores_AdjustMissing)[1]/2),y=-10,label=paste("Deletion Enhancers =",dim(InteractionScores_AdjustMissing[InteractionScores_AdjustMissing$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_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_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_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_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_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_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_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(outDir, "RankPlots_lm.pdf", sep = ""), width = 18, height = 12, onefile = TRUE) - 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() + 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 + ) - 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(outDir,"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) + 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$Avg_Zscore_L), ] - - X_NArm <- InteractionScores[!is.na(InteractionScores$Z_lm_L) | !is.na(InteractionScores$Avg_Zscore_L) ,] - - #find overlaps + # Find overlaps X_NArm$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") + 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) + # 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) + 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) + 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) + get_lm_AUC <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Avg_Zscore_AUC) AUC_lm <- summary(get_lm_AUC) - pdf(paste(outDir,"Avg_Zscore_vs_lm_NA_rm.pdf",sep=""),width = 16, height = 12, onefile = TRUE) + pdf(paste(outDir, "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$r.squared,2))) + theme_Publication_legend_right()) + print(ggplot(X_NArm, aes(Avg_Zscore_L, Z_lm_L)) + + geom_point(aes(color = Overlap), shape = 3) + + geom_smooth(method = "lm", color = 1) + + ggtitle("Avg Zscore vs lm L") + + geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm$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$r.squared,2))) + theme_Publication_legend_right()) + print(ggplot(X_NArm, aes(Avg_Zscore_K, Z_lm_K)) + + geom_point(aes(color = Overlap), shape = 3) + + geom_smooth(method = "lm", color = 1) + + ggtitle("Avg Zscore vs lm K") + + geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(K_lm$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$r.squared,2))) + theme_Publication_legend_right()) + print(ggplot(X_NArm, aes(Avg_Zscore_r, Z_lm_r)) + + geom_point(aes(color = Overlap), shape = 3) + + geom_smooth(method = "lm", color = 1) + + ggtitle("Avg Zscore vs lm r") + + geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(r_lm$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$r.squared,2))) + theme_Publication_legend_right()) + print(ggplot(X_NArm, aes(Avg_Zscore_AUC, Z_lm_AUC)) + + geom_point(aes(color = Overlap), shape = 3) + + geom_smooth(method = "lm", color = 1) + + ggtitle("Avg Zscore vs lm AUC") + + geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(AUC_lm$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$r.squared,2))) + theme_Publication_legend_right() + lm_v_Zscore_L <- ggplot(X_NArm, aes(Avg_Zscore_L, Z_lm_L, ORF = OrfRep, Gene = Gene, NG = NG, SM = SM, DB = DB)) + + geom_point(aes(color = Overlap), shape = 3) + + geom_smooth(method = "lm", color = 1) + ggtitle("Avg Zscore vs lm L") + + geom_rect(aes(xmin = -2, xmax = 2, ymin = -2, ymax = 2), color = "grey20", size = 0.25, alpha = 0.1, inherit.aes = FALSE, fill = NA) + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm$r.squared, 2))) + theme_publication_legend_right() pgg <- ggplotly(lm_v_Zscore_L) - #pgg - plotly_path <- paste(outDir,"Avg_Zscore_vs_lm_NA_rm.html",sep="") - saveWidget(pgg, file=plotly_path, selfcontained =TRUE) + plotly_path <- paste(outDir, "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) + # 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) + 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) + 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) + 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 - num_genes_NArm2 <- (dim(X_NArm)[1])/2 + pdf(paste(outDir, "Avg_Zscore_vs_lm_ranked_NA_rm.pdf", sep = ""), width = 16, height = 12, onefile = TRUE) - pdf(paste(outDir,"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$r.squared, 2))) + + theme_publication_legend_right() + ) - print(ggplot(X_NArm,aes(L_Rank,L_Rank_lm)) + geom_point(aes(color=Overlap),shape=3) + geom_smooth(method = "lm",color=1) + - ggtitle("Rank Avg Zscore vs lm L") + - annotate("text",x=num_genes_NArm2,y=num_genes_NArm2,label = paste("R-squared = ",round(L_lm2$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$r.squared,2))) + theme_Publication_legend_right()) + print( + ggplot(X_NArm, aes(K_Rank, K_Rank_lm)) + + geom_point(aes(color = Overlap), shape = 3) + + geom_smooth(method = "lm", color = 1) + + ggtitle("Rank Avg Zscore vs lm K") + + annotate("text", x = num_genes_NArm2, y = num_genes_NArm2, label = paste("R-squared = ", round(K_lm2$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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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() - 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() + pdf(paste(outDir, "RankPlots_naRM.pdf", sep = ""), width = 18, height = 12, onefile = TRUE) - - 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(outDir,"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) + 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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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(outDir, "RankPlots_lm_naRM.pdf", sep = ""), width = 18, height = 12, onefile = TRUE) - 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() + 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 + ) - 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(outDir,"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) + 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) +# 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) +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) +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) +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) +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) +get_lm_6 <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Z_lm_r) L_lm_6 <- summary(get_lm_6) +pdf(file = paste(outDir, "Correlation_CPPs.pdf", sep = ""), width = 10, height = 7, onefile = TRUE) -pdf(file=paste(outDir,"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$r.squared, 3))) + + theme_publication_legend_right() + + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_text(size = 16), + axis.title.x = element_text(size = 18), + axis.text.y = element_text(size = 16), + axis.title.y = element_text(size = 18) + ) -ggplot(X_NArm,aes(Z_lm_L,Z_lm_K)) + geom_point(shape=3,color="gray70") + - geom_smooth(method="lm",color="tomato3") + - ggtitle("Interaction L vs. Interaction K") + - xlab("z-score L") + ylab("z-score K") + - annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_1$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$r.squared, 3))) + + theme_publication_legend_right() + + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_text(size = 16), + axis.title.x = element_text(size = 18), + axis.text.y = element_text(size = 16), + axis.title.y = element_text(size = 18) + ) -ggplot(X_NArm,aes(Z_lm_L,Z_lm_r)) + geom_point(shape=3,color="gray70") + - geom_smooth(method="lm",color="tomato3") + - ggtitle("Interaction L vs. Interaction r") + - xlab("z-score L") + ylab("z-score r") + - annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_2$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$r.squared, 3))) + +theme_publication_legend_right() + +theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_text(size = 16), + axis.title.x = element_text(size = 18), + axis.text.y = element_text(size = 16), + axis.title.y = element_text(size = 18) +) -ggplot(X_NArm,aes(Z_lm_L,Z_lm_AUC)) + geom_point(shape=3,color="gray70") + - geom_smooth(method="lm",color="tomato3") + - ggtitle("Interaction L vs. Interaction AUC") + - xlab("z-score L") + ylab("z-score AUC") + - annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_3$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$r.squared, 3))) + + theme_publication_legend_right() + + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_text(size = 16), + axis.title.x = element_text(size = 18), + axis.text.y = element_text(size = 16), + axis.title.y = element_text(size = 18) + ) -ggplot(X_NArm,aes(Z_lm_K,Z_lm_r)) + geom_point(shape=3,color="gray70") + - geom_smooth(method="lm",color="tomato3") + - ggtitle("Interaction K vs. Interaction r") + - xlab("z-score K") + ylab("z-score r") + - annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_4$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$r.squared, 3))) + + theme_publication_legend_right() + + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_text(size = 16), + axis.title.x = element_text(size = 18), + axis.text.y = element_text(size = 16), + axis.title.y = element_text(size = 18) + ) -ggplot(X_NArm,aes(Z_lm_K,Z_lm_AUC)) + geom_point(shape=3,color="gray70") + - geom_smooth(method="lm",color="tomato3") + - ggtitle("Interaction K vs. Interaction AUC") + - xlab("z-score K") + ylab("z-score AUC") + - annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_5$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$r.squared, 3))) + + theme_publication_legend_right() + + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_text(size = 16), + axis.title.x = element_text(size = 18), + axis.text.y = element_text(size = 16), + axis.title.y = element_text(size = 18)) -ggplot(X_NArm,aes(Z_lm_r,Z_lm_AUC)) + geom_point(shape=3,color="gray70") + - geom_smooth(method="lm",color="tomato3") + - ggtitle("Interaction r vs. Interaction AUC") + - xlab("z-score r") + ylab("z-score AUC") + - annotate("text",x=0,y=0,label = paste("R-squared = ",round(L_lm_6$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$Z_lm_L), ] -InteractionScores_RF2 <- InteractionScores_RF[!is.na(InteractionScores_RF$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$r.squared,3))) + - theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=16),axis.title.x = element_text(size=18), axis.text.y = element_text(size=16),axis.title.y = element_text(size=18)) +ggplot(X_NArm, aes(Z_lm_L, Z_lm_K)) + + geom_point(shape = 3, color = "gray70") + + geom_point(data = InteractionScores_RF2, aes(Z_lm_L, Z_lm_K), color = "cyan") + + ggtitle("Interaction L vs. Interaction K") + + xlab("z-score L") + + ylab("z-score K") + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_1$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$r.squared,3))) + - theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=16),axis.title.x = element_text(size=18), axis.text.y = element_text(size=16),axis.title.y = element_text(size=18)) +ggplot(X_NArm, aes(Z_lm_L, Z_lm_r)) + + geom_point(shape = 3, color = "gray70") + + geom_point(data = InteractionScores_RF2, aes(Z_lm_L, Z_lm_r), color = "cyan") + + ggtitle("Interaction L vs. Interaction r") + + xlab("z-score L") + + ylab("z-score r") + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_2$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$r.squared,3))) + - theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=16),axis.title.x = element_text(size=18), axis.text.y = element_text(size=16),axis.title.y = element_text(size=18)) +ggplot(X_NArm, aes(Z_lm_L, Z_lm_AUC)) + + geom_point(shape = 3, color = "gray70") + + geom_point(data = InteractionScores_RF2, aes(Z_lm_L, Z_lm_AUC), color = "cyan") + + ggtitle("Interaction L vs. Interaction AUC") + + xlab("z-score L") + + ylab("z-score AUC") + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_3$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$r.squared,3))) + - theme_Publication_legend_right() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=16),axis.title.x = element_text(size=18), axis.text.y = element_text(size=16),axis.title.y = element_text(size=18)) +ggplot(X_NArm, aes(Z_lm_K, Z_lm_r)) + + geom_point(shape = 3, color = "gray70") + + geom_point(data = InteractionScores_RF2, aes(Z_lm_K, Z_lm_r), color = "cyan") + + ggtitle("Interaction K vs. Interaction r") + + xlab("z-score K") + + ylab("z-score r") + + annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(L_lm_4$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)) +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) +# write.csv(Labels, file = paste("../Code/Parameters.csv"), row.names = FALSE) timestamp() diff --git a/workflow/qhtcp-workflow b/workflow/qhtcp-workflow index e22e6b95..c331f95e 100755 --- a/workflow/qhtcp-workflow +++ b/workflow/qhtcp-workflow @@ -36,11 +36,11 @@ print_help() { --module, -m MODULE[,MODULE...] See MODULES section below for list of available modules If no --include is specified, all modules are run - --submodule, -s SUBMODULE "[ARG1],[ARG2]..." (string of comma delimited arguments) - See SUBMODULES section below for list of available modules - See documentation for submodule argument usage + --wrapper, -w WRAPPER "[ARG1],[ARG2]..." (string of comma delimited arguments) + See WRAPPERS section below for list of available modules + See documentation for wrapper argument usage --nomodule, -n MODULE[,MODULE...] - See MODULES and SUBMODULES section below for list of modules to exclude + See MODULES and WRAPPERS section below for list of modules to exclude --markdown Generate the shdoc markdown README.md file for this program --yes, -y, --auto @@ -53,8 +53,8 @@ print_help() { MODULES: ${ALL_MODULES[*]} - SUBMODULES: - ${ALL_SUBMODULES[*]} + WRAPPERS: + ${ALL_WRAPPERS[*]} DEPENDENCIES: deb: ${depends_deb[@]} @@ -71,7 +71,7 @@ print_help() { script-run-workflow --module=${ALL_MODULES[0]},${ALL_MODULES[1]} script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT,${PROJECT_PREFIX}_MY_OTHER_PROJECT script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT,${PROJECT_PREFIX}_MY_OTHER_PROJECT --module=${ALL_MODULES[1]},${ALL_MODULES[2]} --yes --debug - script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT --submodule ${ALL_SUBMODULES[2]} \"/path/to/genefile.txt,/path/to/output/dir\" --submodule ${ALL_SUBMODULES[3]} \"/path/to/sgofile\" + script-run-workflow --project=${PROJECT_PREFIX}_MY_PROJECT --wrapper ${ALL_WRAPPERS[2]} \"/path/to/genefile.txt,/path/to/output/dir\" --wrapper ${ALL_WRAPPERS[3]} \"/path/to/sgofile\" EOF } @@ -136,10 +136,10 @@ print_help() { # # # @description -# `--project`, `--module`, `--nomodule`, and `--submodule` can be passed multiple times or with a comma-separated string +# `--project`, `--module`, `--nomodule`, and `--wrapper` can be passed multiple times or with a comma-separated string # @option -p | --project= One or more projects to analyze, can be passed multiple times or with a comma-separated string # @option -m | --module= One or more modules to run (default: all), can be passed multiple times or with a comma-separated string -# @option -s | --submodule= Requires two arguments: the name of the submodule and its arguments, can be passed multiple times +# @option -w | --wrapper= Requires two arguments: the name of the wrapper and its arguments, can be passed multiple times # @option -n | --nomodule= One or more modules (default: none) to exclude from the analysis # @option --markdown Generate the shdoc markdown file for this program # @option -y | --yes | --auto Assume yes answer to all questions (non-interactive mode) @@ -147,14 +147,14 @@ print_help() { # @option -h | --help Print help message and exit (overrides other options) # @set PROJECTS array List of projects to cycle through # @set MODULES array List of modules to run on each project -# @set SUBMODULES array List of submodules and their arguments to run on each project +# @set WRAPPERS array List of wrappers and their arguments to run on each project # @set EXCLUDE_MODULES array List of modules not to run on each project # @set DEBUG int Turn debugging on # @set YES int Turn assume yes on parse_input() { debug "Running: ${FUNCNAME[0]} $*" - long_opts="project:,module:,submodule:,nomodule:,markdown,yes,auto,debug,help" + long_opts="project:,module:,wrapper:,nomodule:,markdown,yes,auto,debug,help" short_opts="+p:m:s:n:ydh" if input=$(getopt -o $short_opts -l $long_opts -- "$@"); then @@ -169,18 +169,18 @@ parse_input() { shift IFS=',' read -ra MODULES <<< "$1" ;; - --submodule|-s) + --wrapper|-w) shift - IFS=',' read -ra SUBMODULES <<< "$1" + IFS=',' read -ra WRAPPERS <<< "$1" shift - IFS=',' read -ra SUBMODULES <<< "$1" + IFS=',' read -ra WRAPPERS <<< "$1" ;; --nomodule|-n) shift EXCLUDE_MODULES+=("$1") ;; --markdown) - documentation; exit 0 # TODO disable the exit after development + generate_markdown; exit 0 # TODO disable the exit after development ;; --yes|-y|--auto) declare -g YES=1 @@ -212,10 +212,10 @@ parse_input() { # # * Build a new type of analysis from scratch # * Generate project directories -# * Group multiple submodules (and modules) into a larger task -# * Dictate the ordering of multiple submodules -# * Competently handle pushd and popd for their submodules if they do not reside in the SCANS/PROJECT_DIR -# * Call their submodules with the appropriate arguments +# * Group multiple wrappers (and modules) into a larger task +# * Dictate the ordering of multiple wrappers +# * Competently handle pushd and popd for their wrappers if they do not reside in the SCANS/PROJECT_DIR +# * Call their wrappers with the appropriate arguments # # @description module() { @@ -333,7 +333,8 @@ backup() { done echo "Backing up $f to $f.bk.$count" debug "rsync -a $f $f.bk.$count" - rsync -a "$f" "$f.bk.$count" + rsync -a "$f" "$f.bk.$count" && + rm -rf "$f" done } @@ -342,11 +343,11 @@ backup() { interactive_header() { debug "Running: ${FUNCNAME[0]}" - cat <<-'EOF' + cat <<-EOF _ _ _ _ _ | | | | | | | | | | | |__| | __ _ _ __| |_ _ __ ___ __ _ _ __ | | __ _| |__ - | __ |/ _` | `__| __| `_ ` _ \ / _` | `_ \ | | / _` | `_ \ + | __ |/ _ | __| __| _ _ \ / _ | _ \ | | / _ | _ \ | | | | (_| | | | |_| | | | | | (_| | | | | | |___| (_| | |_) | |_| |_|\__,_|_| \__|_| |_| |_|\__,_|_| |_| |______\__,_|_.__/ ___ _ _ _____ ____ ____ @@ -365,92 +366,6 @@ interactive_header() { underline=$(tput smul) nounderline=$(tput rmul) - echo "${underline}Modules${nounderline}" - # Determine if we need two columns - if [[ ${#ALL_MODULES[@]} -gt 8 ]]; then - # Calculate the number of elements in each column - num_columns=$(( (${#ALL_MODULES[@]} + 1) / 2 )) - - # Determine the maximum width of the first column - max_width=0 - for ((i=0; i for the latest project + * for the latest project ($num) * A comma-separated list of project numbers: 2,5,12 * 0 to add a new project EOF @@ -525,7 +525,7 @@ interactive_header() { if [[ $response == 0 ]]; then ask_pn && PROJECTS+=("${ADD_PROJECTS[@]}") else - [[ -z $response ]] && response=$num + [[ -z $response ]] && response="$num" IFS=',' read -ra arr <<< "$response" for i in "${arr[@]}"; do if [[ -n ${projects[$((i-1))]} ]]; then @@ -553,7 +553,7 @@ interactive_header() { cat <<-EOF ${underline}Enter modules(s) to run${nounderline} * for all - * A comma-separated list of module numbers (2,5,12) + * A comma-separated list of module numbers: 2,5,12 * 0 for none EOF ((YES)) || read -r -p "(all): " response @@ -565,28 +565,32 @@ interactive_header() { else IFS=',' read -ra arr <<< "$response" for i in "${arr[@]}"; do - MODULES+=("${ALL_MODULES[$((i-1))]}") + if [[ $i =~ ^[0-9]+$ ]]; then + MODULES+=("${ALL_MODULES[$((i-1))]}") + else + err "Module number $i is invalid, skipping" + fi done fi unset response arr i fi - if [[ ${#MODULES[@]} -eq 0 && ${#EXCLUDE_MODULES[@]} -eq 0 && ${#SUBMODULES[@]} -eq 0 ]]; then + if [[ ${#MODULES[@]} -eq 0 && ${#EXCLUDE_MODULES[@]} -eq 0 && ${#WRAPPERS[@]} -eq 0 ]]; then while :; do cat <<-EOF - ${underline}Enter submodules(s) to run followed by its arguments in a comma-separated string${nounderline} + ${underline}Enter wrapper(s) to run followed by its arguments in a comma-separated string${nounderline} * for none - * A comma-separated list of submodules and their arguments + * A comma-separated list of wrappers and their arguments * Both arguments are required * Quote the argument string if it contains whitespace - * Example: ${ALL_SUBMODULES[0]},\"arg1,arg2,arg3...\",${ALL_SUBMODULES[1]},\"arg1,arg2,arg3...\" + * Example: ${ALL_WRAPPERS[0]},\"arg1,arg2,arg3...\",${ALL_WRAPPERS[1]},\"arg1,arg2,arg3...\" EOF ((YES)) || read -r -p "(none): " response echo "" [[ -z $response ]] && break IFS=',' read -ra arr <<< "$response" if [[ $((${#arr[@]} % 2)) -eq 0 ]]; then # check if array is even - SUBMODULES+=("${arr[@]}") + WRAPPERS+=("${arr[@]}") else err "The second argument is required (may be an empty string, \"\")" fi @@ -639,9 +643,9 @@ install_dependencies() { debug "Running: ${FUNCNAME[0]} $*" # Dependency arrays - depends_rpm=(graphviz pandoc pdftk-java gd-devel perl-CPAN shdoc nano rsync coreutils libcurl-devel openssl-devel harfbuzz-devel fribidi-devel) - depends_deb=(graphviz pandoc pdftk-java libgd-dev perl shdoc nano rsync coreutils libcurl-dev openssl-dev libharfbuzz-dev libfribidi-dev) - depends_brew=(graphiz pandoc gd pdftk-java shdoc nano perl rsync coreutils harfbuzz fribidi) + depends_rpm=(graphviz pandoc pdftk-java gd-devel perl-CPAN shdoc nano rsync coreutils libcurl-devel openssl-devel harfbuzz-devel fribidi-devel R-core R-core-devel) + depends_deb=(graphviz pandoc pdftk-java libgd-dev perl shdoc nano rsync coreutils libcurl-dev openssl-dev libharfbuzz-dev libfribidi-dev r-base r-base-dev) + depends_brew=(graphiz pandoc gd pdftk-java shdoc nano perl rsync coreutils harfbuzz fribidi r) depends_perl=(Test::Warnings Test::Fatal File::Map Sub::Uplevel ExtUtils::Config ExtUtils::PkgConfig IPC::Run Module::Build::Tiny IPC::Run GD GO::TermFinder) depends_r=(BiocManager ontologyIndex ggrepel tidyverse sos openxlsx ggplot2 plyr extrafont gridExtra gplots stringr plotly ggthemes pandoc rmarkdown @@ -744,6 +748,7 @@ module init_project # * MasterPlate_ file **should not be an xlsx file**, no portability # * We can keep the existing xlsx code for old style fallback # * But moving forward should switch to csv or something open +# * Do we need to sync a QHTCP template? # # NOTES # @@ -757,29 +762,165 @@ module init_project init_project() { debug "Running: ${FUNCNAME[0]}" - # We handle this in main() and pushd to it - # But do it one more time in case this is run as a module - - ask "(Re)-Initialize a project at $PROJECT_SCANS_DIR?" || return 1 + # Create a project scans dir [[ -d $PROJECT_SCANS_DIR ]] || (mkdir -p "$PROJECT_SCANS_DIR" || return 1) + # TODO eventually copy scans from robot but for now let's pause and wait for transfer + echo "In the future we will copy scans from robot here" + # read -r -p "Hit to continue: " + + # Create the QHTCP out directory + if [[ -d $QHTCP_RESULTS_DIR ]]; then + # Handle existing output directory + cat <<-EOF + A project already exists at $QHTCP_RESULTS_DIR + Would you like to + * (u)pdate it from the template and continue + * (b)ack it up and start from scratch + * (c)ontinue (default) + EOF + + for i in {1..3}; do # give the user three chances to get it right + ((YES)) || read -r -p "(c): " response + echo "" + [[ -z $response ]] && break + case $response in + u) + echo "Updating project from template" + echo "Only files that are newer in the template will be overwritten" + if rsync --archive --update "$QHTCP_TEMPLATE_DIR"/ "$QHTCP_RESULTS_DIR"; then + echo "Project updated with template" + fi + ;; + b) + if backup "$QHTCP_RESULTS_DIR"; then + mkdir "$QHTCP_RESULTS_DIR" + rsync --archive "$QHTCP_TEMPLATE_DIR"/ "$QHTCP_RESULTS_DIR" + echo "Created new project at $QHTCP_RESULTS_DIR" + fi + ;; + c) + break + ;; + *) + err "Invalid response, please try again" + continue + ;; + esac + break + done + else + echo "Creating project results dir at $QHTCP_RESULTS_DIR" + debug "mkdir $QHTCP_RESULTS_DIR" + mkdir "$QHTCP_RESULTS_DIR" + debug "rsync --archive $QHTCP_TEMPLATE_DIR/ $QHTCP_RESULTS_DIR" + rsync --archive "$QHTCP_TEMPLATE_DIR"/ "$QHTCP_RESULTS_DIR" + fi # Create the study info file - if [[ ! -f $STUDY_INFO_FILE ]]; then - echo "Create a default study info file?"; - if ask "You can edit this file in the qhtcp module"; then - cat <<-EOF > "$STUDY_INFO_FILE" - "ExpNumb","ExpLabel","BackgroundSD","ZscoreJoinSD","AnalysisBy" - - EOF - fi + # Use initials from project or whoami? + # Best I can do is first two letters of username + # See TODO in markdown + initials="${USER:0:2}" + INITIALS=${initials^^} + + empty_study=1 + # Find an Exp directory that does not exist + while [[ -d $QHTCP_RESULTS_DIR/Exp$empty_study ]]; do + (( empty_study++ )) + done + + next_study_entry="$empty_study,$PROJECT_SUFFIX,NA,NA,$INITIALS" + + echo "${underline}Study Info File${nounderline}" + + if [[ -f $STUDY_INFO_FILE ]]; then + # Get latest entry + get_studies "$STUDY_INFO_FILE" + largest=${STUDIES_NUMS[0]} + for i in "${STUDIES_NUMS[@]}"; do + if ((i > largest)); then + largest=$i + fi + done + empty_study=$((largest+1)) + next_study_entry="$((empty_study)),$PROJECT_SUFFIX,NA,NA,$INITIALS" + else # create a default StudyInfo.csv + echo "ExpNumb,ExpLabel,BackgroundSD,ZscoreJoinSD,AnalysisBy" > "$STUDY_INFO_FILE" + echo "$next_study_entry" >> "$STUDY_INFO_FILE" + next_study_entry="$((empty_study+1)),$PROJECT_SUFFIX,NA,NA,$INITIALS" fi + # Print current studies + cat <<-EOF + * Give each experiment labels to be used for the plots and specific files. + * Enter the desired experiment names in the order they should appear in the REMc heatmaps + + Current study info file contents: + + ${underline}$STUDY_INFO_FILE${nounderline} + $(cat "$STUDY_INFO_FILE") + + EOF + + # Allow user to add/edit the study info file + if ! ((YES)); then + for ((i=1; i<2; i++)); do + cat <<-EOF + Next entry suggestion: "$next_study_entry" + + Would you like to: + * (a)dd the suggested entry + * (e)dit the StudyInfo.csv file manually + * (c)ontinue (default) + EOF + read -r -p "(c): " response + echo "" + [[ -z $response ]] && break + case $response in + a) + echo "Adding auto-entry suggestion to $STUDY_INFO_FILE" + echo "$next_study_entry" >> "$STUDY_INFO_FILE" + next_study_entry="$((empty_study+1)),$PROJECT_SUFFIX,NA,NA,$INITIALS" + i=0 + ;; + e) + debug "${EDITOR:-nano} $STUDY_INFO_FILE" + ${EDITOR:-nano} "$STUDY_INFO_FILE" + ;; + c) + break + ;; + *) + err "Invalid response, please try again" + i=0 + ;; + esac + break + done + fi + + get_studies "$STUDY_INFO_FILE" + + # Initialize missing Exp dirs + STUDIES_DIRS=() + for s in "${STUDIES_NUMS[@]}"; do + study_dir="$QHTCP_RESULTS_DIR/Exp$s" + STUDIES_DIRS+=("$study_dir") + [[ -d $study_dir ]] || mkdir "$study_dir" + # # We don't need a template anymore? + # if ! rsync --archive "$STUDY_TEMPLATE_DIR" "$STUDY_DIR"; then + # err "Could not copy $STUDY_TEMPLATE_DIR template to $STUDY_DIR" + # continue + # fi + # fi + done + # Write skeleton files in csv # If we have to convert to xlsx later, so be it - echo "In the future we will copy the DrugMedia file from robot here" + echo "In the future, create the DrugMedia file here" # cat <<-EOF > "$DRUG_MEDIA_FILE" @@ -788,7 +929,7 @@ init_project() { # TODO here we'll copy scans from robot but for now let's pause and wait for transfer - echo "In the future we will copy the MasterPlate file from robot here" + echo "In the future, create the MasterPlate file here" # cat <<-EOF > "$MASTER_PLATE_FILE" @@ -796,9 +937,7 @@ init_project() { # EOF - # TODO here we'll copy scans from robot but for now let's pause and wait for transfer - echo "In the future we will copy scans from robot here" - read -r -p "Hit to continue: " + } @@ -807,6 +946,15 @@ module easy # @description # Run the EASY matlab program # +# INPUT FILES +# * MasterPlate_.xls +# * DrugMedia_.xls +# +# OUTPUT FILES +# * !!ResultsStd_.txt +# * !!ResultsELr_.txt +# +# # TODO # # * Don't create output in the scans folder, put it in an output directory @@ -1131,11 +1279,10 @@ easy() { EOF declare -gx EASY_DIR="$APPS_DIR/matlab/easy" - declare -gx EASY_RESULTS_DIR="$EASY_OUT_DIR/$PROJECT_PREFIX" script="$EASY_DIR/EASYconsole.m" # Prompt user for suffix - echo "Using EASY results directory: $EASY_RESULTS_DIR" + echo "Current EASY results directory: $EASY_RESULTS_DIR" ((YES)) || read -r -p "Enter a custom suffix and/or hit enter to use the default directory (no suffix): " EASY_SUFFIX [[ -n $EASY_SUFFIX ]] && EASY_RESULTS_DIR+="_$EASY_SUFFIX" @@ -1157,7 +1304,7 @@ easy() { done # Copy Templates - declare -gx DRUG_MEDIA_FILE="$EASY_RESULTS_DIR/DrugMedia_$PROJECT_NAME.xls" + declare -gx DRUG_MEDIA_FILE="$SCANS_DIR/DrugMedia_$PROJECT_NAME.xls" declare -gx MASTER_PLATE_FILE="$EASY_RESULTS_DIR/MasterPlate_$PROJECT_NAME.xls" rsync -a "$EASY_DIR"/{figs,PTmats} "$EASY_RESULTS_DIR" @@ -1262,53 +1409,14 @@ module qhtcp qhtcp() { debug "Running: ${FUNCNAME[0]}" - if [[ -d $QHTCP_PROJECT_DIR ]]; then - # Handle existing output directory - cat <<-EOF - A project already exists at $QHTCP_PROJECT_DIR - Would you like to - * (u)pdate it from the template and continue - * (b)ack it up and start from scratch - * (c)ontinue (default) - EOF - - for i in {1..3}; do # give the user three chances to get it right - ((YES)) || read -r -p "(c): " response - echo "" - [[ -z $response ]] && break - case $response in - u) - echo "Updating project from template" - echo "Only files that are newer in the template will be overwritten" - if rsync --archive --update "$QHTCP_TEMPLATE_DIR"/ "$QHTCP_PROJECT_DIR"; then - echo "Project updated with template" - fi - ;; - b) - backup "$QHTCP_PROJECT_DIR" && rm -rf "$QHTCP_PROJECT_DIR" - if rsync --archive --update "$QHTCP_TEMPLATE_DIR"/ "$QHTCP_PROJECT_DIR"; then - echo "New project created at $QHTCP_PROJECT_DIR" - fi - ;; - c) - break - ;; - *) - err "Invalid response, please try again" - continue - ;; - esac - break - done - fi + [[ -d $QHTCP_RESULTS_DIR ]] || + err "$QHTCP_RESULTS_DIR does not exist, have you run the init_project module?" # Sets STUDIES_NUMS get_studies "$STUDY_INFO_FILE" + choose_easy_results "$EASY_OUT_DIR" - # Replacing ExpFrontend.m - # choose_easy_results_dir - # # Create studies archive file if missing # if ! [[ -d $STUDIES_ARCHIVE_FILE ]]; then # header=(StudyDate tStudyName StudyPath ExpNum ExpDate ExpPath ResultFile) @@ -1320,7 +1428,7 @@ qhtcp() { # for s in "${STUDIES_NUMS[@]}"; do # # Trying to match old ExpFrontend formatting # printf "%s\t" \ - # "${DATE//_/}" "$PROJECT_NAME" "$QHTCP_PROJECT_DIR" "Exp$s" \ + # "${DATE//_/}" "$PROJECT_NAME" "$QHTCP_RESULTS_DIR" "Exp$s" \ # "$PROJECT_DATE" "$PROJECT_SCANS_DIR" "$EASY_RESULTS_DIR" "${f##*/}" \ # >> "$STUDIES_ARCHIVE_FILE" # done @@ -1328,15 +1436,18 @@ qhtcp() { # Run R interactions script on all studies for s in "${STUDIES_NUMS[@]}"; do - STUDY_DIR="$QHTCP_PROJECT_DIR/Exp$s" + STUDY_DIR="$QHTCP_RESULTS_DIR/Exp$s" r_interactions \ - "$STUDY_DIR" \ + "$EASY_RESULTS_DIR/results_std.txt" \ + "$STUDY_DIR/zscores" \ "$STUDY_INFO_FILE" \ - "$STUDY_DIR/zscores/" \ "$APPS_DIR/r/SGD_features.tab" \ - 3 + 3 \ + "$s" done + read -r -p "Press enter to continue" response + # Run remc as part of the QHTCP process # pass all the study directories to it so the scripts have all the paths remc "$STUDY_INFO_FILE" "${STUDIES_DIRS[@]}" @@ -1353,33 +1464,33 @@ module remc remc() { debug "Running: ${FUNCNAME[0]} $*" - # If any submodules fail the rest will not run, this is fundamental to module design + # If any wrappers fail the rest will not run, this is fundamental to module design # Remove leading && to run regardless # TODO can this be r_join_interactions \ - "$QHTCP_PROJECT_DIR/out" # output directory + "$QHTCP_RESULTS_DIR" # output directory 2 \ % sd value "$1" # studyInfo file "${@:2}" \ && java_extract \ "$APPS_DIR/java/GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab" \ "$APPS_DIR/java/ORF_List_Without_DAmPs.txt" \ - "$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv" \ - "$QHTCP_PROJECT_DIR" \ - "$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv-finalTable.csv" \ + "$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv" \ + "$QHTCP_RESULTS_DIR" \ + "$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv-finalTable.csv" \ && r_add_shift_values \ - "$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv-finalTable.csv" \ - "$QHTCP_PROJECT_DIR/Shift_only.csv" \ + "$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv-finalTable.csv" \ + "$QHTCP_RESULTS_DIR/Shift_only.csv" \ "$1" \ - "$QHTCP_PROJECT_DIR/REMcWithShift.csv" \ + "$QHTCP_RESULTS_DIR/REMcWithShift.csv" \ && r_create_heat_maps \ - "$QHTCP_PROJECT_DIR/REMcWithShift.csv" \ - "$QHTCP_PROJECT_DIR" \ + "$QHTCP_RESULTS_DIR/REMcWithShift.csv" \ + "$QHTCP_RESULTS_DIR" \ && r_heat_maps_homology \ - "$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv-finalTable.csv" \ + "$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv-finalTable.csv" \ "$APPS_DIR/r/170503_DAmPs_Only.txt" \ "$APPS_DIR/r/Yeast_Human_Homology_Mapping_biomaRt_18_0920.csv" \ - "$QHTCP_PROJECT_DIR/out/homology" + "$QHTCP_RESULTS_DIR/homology" } @@ -1391,7 +1502,7 @@ module gtf # @arg $4 string ORF_List_Without_DAmPs.txt gtf() { debug "Running: ${FUNCNAME[0]}" - gtf_out_dir="${1:-$QHTCP_PROJECT_DIR/out/gtf}" + gtf_out_dir="${1:-$QHTCP_RESULTS_DIR/gtf}" gene_association_sgd="${2:-"$APPS_DIR/r/gene_association.sgd"}" gene_ontology_obo="${3:-"$APPS_DIR/r/gene_ontology_edit.obo"}" orf_list="${4:-"$APPS_DIR/r/ORF_List_Without_DAmPs.txt"}" @@ -1451,7 +1562,7 @@ module gta gta() { debug "Running: ${FUNCNAME[0]}" - gta_out_dir="${1:-"$QHTCP_PROJECT_DIR/gta"}" + gta_out_dir="${1:-"$QHTCP_RESULTS_DIR/gta"}" gene_association_sgd="${2:-"$APPS_DIR/r/gene_association.sgd"}" gene_ontology_obo="${3:-"$APPS_DIR/r/gene_ontology_edit.obo"}" sgd_terms_tfile="${4:-"$APPS_DIR/r/go_terms.tab"}" @@ -1488,7 +1599,7 @@ gta() { # Individual studies for s in "${STUDIES_NUMS[@]}"; do - zscores_file="$QHTCP_PROJECT_DIR/Exp$s/$zscores_file" + zscores_file="$QHTCP_RESULTS_DIR/Exp$s/$zscores_file" if [[ -f $zscores_file ]]; then mkdir "$gta_out_dir/Exp$s" r_gta \ @@ -1516,32 +1627,30 @@ gta() { "$sgd_terms_tfile" \ "$all_sgd_terms_csv" \ "$zscores_file" \ - "$QHTCP_PROJECT_DIR" \ - "$QHTCP_PROJECT_DIR/TermSpecificHeatmaps" \ + "$QHTCP_RESULTS_DIR" \ + "$QHTCP_RESULTS_DIR/TermSpecificHeatmaps" \ "${STUDIES_NUMS[@]}" } -# @section Submodules +# @section Wrappers # @description # -# Submodules are shell wrappers for workflow components in external languages -# -# Submodules: +# Wrappers: # # * Allow scripts to be called by the main workflow script using input and output arguments as a translation mechanism. # * Only run by default if called by a module. # * Can be called directly with its arguments as a comma-separated string # # @description -submodule() { - debug "Adding $1 submodule" - ALL_SUBMODULES+=("$1") +wrapper() { + debug "Adding $1 wrapper" + ALL_WRAPPERS+=("$1") declare -gA "$1" } -submodule r_gta +wrapper r_gta # @description GTAtemplate R script # # TODO @@ -1582,7 +1691,7 @@ r_gta() { } -submodule r_gta_pairwiselk +wrapper r_gta_pairwiselk # @description PairwiseLK.R R script # # TODO @@ -1598,7 +1707,7 @@ submodule r_gta_pairwiselk # # * # -# This submodule: +# This wrapper: # # * Will perform both L and K comparisons for the specified experiment folders. # * The code uses the naming convention of PairwiseCompare_Exp’#’-Exp’#’ to standardize and keep simple the structural naming (where ‘X’ is either K or L and ‘Y’ is the number of the experiment GTA results to be found in ../GTAresult/Exp_). @@ -1624,7 +1733,7 @@ r_gta_pairwiselk() { } -submodule r_gta_heatmaps +wrapper r_gta_heatmaps # @description TSHeatmaps5dev2.R R script # # TODO @@ -1642,7 +1751,7 @@ submodule r_gta_heatmaps # # * # -# This submodule: +# This wrapper: # # * The Term Specific Heatmaps are produced directly from the ../ExpStudy/Exp_/ZScores/ZScores_Interaction.csv file generated by the user modified interaction… .R script. # * The heatmap labeling is per the names the user wrote into the StudyInfo.txt spreadsheet. @@ -1670,50 +1779,60 @@ r_gta_heatmaps() { } -submodule r_interactions -# @description Run the R interactions analysis (Z_InteractionTemplate.R) +wrapper r_interactions +# @description Run the R interactions analysis (deprecates Z_InteractionTemplate.R) +# +# SCRIPT: interactions.R # # TODO # -# * Don't want to rename Z_InteractionTemplate.R because that will break logic, just edit in place instead +# * Parallelization (need to consult with Sean) +# * Needs more loops to reduce verbosity, but don't want to limit flexibility +# * Replace 1:length() with seq_along() +# * Re-enable disabled linter checks +# * Reduce cyclomatic complexity of some of the for loops # # NOTES # # * # # @arg $1 string The input directory -# @arg $2 string The study info file -# @arg $3 string The zscores directory +# @arg $2 string The zscores directory +# @arg $3 string The study info file # @arg $4 string SGD_features.tab # @arg $5 integer delta SD background value (default: 5) +# @arg $6 integer experiment number r_interactions() { - debug "Running: ${FUNCNAME[0]}" + debug "Running: ${FUNCNAME[0]} $*" cat <<-EOF - Edit the Z_InteractionTemplate.R script in each Exp dir beginning at the '++BEGIN USER DATA SELECTION++' - This is designed so that the data of interest for each experiment is appropriately selected from the !!Results…txt file + * Be sure to enter Background noise filter standard deviation i.e., 3 or 5 per Sean + * Enter Standard deviation value for removing data for cultures due to high background (e.g., contaminated cultures). + * Generally set this very high (e.g., '20') on the first run in order NOT to remove data, e.g. '20'. Review QC data and inspect raw image data to decide if it is desirable to remove data, and then rerun analysis. + * Enter a Background SD threshold for EXCLUDING culture data from further analysis: + * This Background value removes data where there is high pixel intensity in the background regions of a spot culture (i.e., suspected contamination). + * 5 is a minimum recommended value, because lower values result in more data being removed, and often times this is undesirable if contamination occurs late after the carrying capacity of the yeast culture is reached. + * This is most often "trial and error", meaning there is a 'Frequency_Delta_Background.pdf' report in the /Exp_/ZScores/QC/ folder to evaluate whether the chosen value was suitable (and if not the analysis can simply be rerun with a more optimal choice). + * In general, err on the high side, with BSD of 10 or 12…. One can also use EZview to examine the raw images and individual cultures potentially included/excluded as a consequence of the selected value. + * Background values are reported in the results sheet and so could also be analyzed there. - Be sure to enter Background noise filter standard deviation i.e., 3 or 5 per Sean - Enter Standard deviation value for removing data for cultures due to high background (e.g., contaminated cultures). Generally set this very high (e.g., '20') on the first run in order NOT to remove data, e.g. '20'. Review QC data and inspect raw image data to decide if it is desirable to remove data, and then rerun analysis. - Enter a Background SD threshold for EXCLUDING culture data from further analysis: - This Background value removes data where there is high pixel intensity in the background regions of a spot culture (i.e., suspected contamination). 5 is a minimum recommended value, because lower values result in more data being removed, and often times this is undesirable if contamination occurs late after the carrying capacity of the yeast culture is reached. - This is most often "trial and error", meaning there is a 'Frequency_Delta_Background.pdf' report in the /Exp_/ZScores/QC/ folder to evaluate whether the chosen value was suitable (and if not the analysis can simply be rerun with a more optimal choice). In general, err on the high side, with BSD of 10 or 12…. One can also use EZview to examine the raw images and individual cultures potentially included/excluded as a consequence of the selected value. Background values are reported in the results sheet and so could also be analyzed there.. EOF script="$APPS_DIR/r/interactions.R" - debug "$RSCRIPT $script" "$@" + debug "$RSCRIPT $script $*" "$RSCRIPT" "$script" \ "$1" \ "$2" \ "$3" \ "${4:-"$APPS_DIR/r/SGD_features.tab"}" \ - "${5:-5}" \ - "${@:6}" # optional arguments + "${5:-3}" \ + "${6}" \ + "${@:7}" # optional arguments } -submodule r_join_interactions +wrapper r_join_interactions # @description JoinInteractExps3dev.R creates REMcRdy_lm_only.csv and Shift_only.csv # # Output @@ -1741,7 +1860,7 @@ r_join_interactions() { } -submodule java_extract +wrapper java_extract # @description Jingyu's REMc java utility # # Input @@ -1769,24 +1888,24 @@ java_extract() { classpath="$APPS_DIR/java/javaExtract.jar" # backup previous output - if ! backup "${5:-"$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv-finalTable.csv"}"; then - ask "Backup of ${5:-"$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv-finalTable.csv"} failed, continue?" || return 1 + if ! backup "${5:-"$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv-finalTable.csv"}"; then + ask "Backup of ${5:-"$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv-finalTable.csv"} failed, continue?" || return 1 fi java_cmd=( "$JAVA" -Xms512m -Xmx2048m -Dfile.encoding=UTF-8 -classpath "$classpath" ExecMain - "${3:-"$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv"}" + "${3:-"$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv"}" "${1:-"$APPS_DIR/java/GeneByGOAttributeMatrix_nofiltering-2009Dec07.tab"}" "${2:-"$APPS_DIR/java/ORF_List_Without_DAmPs.txt"}" ) debug "pushd && ${java_cmd[*]} && popd" - pushd "${4:-"$QHTCP_PROJECT_DIR"}" && "${java_cmd[@]}" && popd || return 1 - [[ -f ${5:-$QHTCP_PROJECT_DIR/REMcRdy_lm_only.csv-finalTable.csv} ]] + pushd "${4:-"$QHTCP_RESULTS_DIR"}" && "${java_cmd[@]}" && popd || return 1 + [[ -f ${5:-$QHTCP_RESULTS_DIR/REMcRdy_lm_only.csv-finalTable.csv} ]] } -submodule r_add_shift_values +wrapper r_add_shift_values # @description Add shift values back to REMcRdy_lm_only.csv-finalTable.csv # and output "REMcWithShift.csv" for use with the REMc heat maps # @arg $1 string REMcRdy_lm_only.csv-finalTable.csv @@ -1803,13 +1922,13 @@ r_add_shift_values() { "$3" \ "$4" \ "${@:5}" # optional arguments - rm -f "$QHTCP_PROJECT_DIR/REMcHeatmaps/"*.pdf - out_file="$QHTCP_PROJECT_DIR/REMcWithShift.csv" + rm -f "$QHTCP_RESULTS_DIR/REMcHeatmaps/"*.pdf + out_file="$QHTCP_RESULTS_DIR/REMcWithShift.csv" [[ -f $out_file ]] || (echo "$out_file does not exist"; return 1) } -submodule r_create_heat_maps +wrapper r_create_heat_maps # @description Execute createHeatMaps.R # @arg $1 string The final shift table (REMcWithShift.csv) # @arg $2 string The output directory @@ -1829,7 +1948,7 @@ r_create_heat_maps() { } -submodule r_heat_maps_homology +wrapper r_heat_maps_homology # @description Execute createHeatMapsAll.R # @arg $1 string REMcRdy_lm_only.csv-finalTable.csv # @arg $2 string Shift_only.csv @@ -1857,7 +1976,7 @@ r_heat_maps_homology() { } -submodule py_gtf_dcon +wrapper py_gtf_dcon # @description Perform python dcon portion of GTF # # Output @@ -1878,8 +1997,8 @@ py_gtf_dcon() { } -submodule pl_gtf_analyze -# @description Perl analyze submodule +wrapper pl_gtf_analyze +# @description Perl analyze wrapper # This seems weird to me because we're just overwriting the same data for all set2 members # https://metacpan.org/dist/GO-TermFinder/view/examples/analyze.pl # Is there a reason you need a custom version and not the original from cpan? @@ -1900,8 +2019,8 @@ pl_gtf_analyze() { } -submodule pl_gtf_terms2tsv -# @description Perl terms2tsv submodule +wrapper pl_gtf_terms2tsv +# @description Perl terms2tsv wrapper # Probably should be translated to shell/python # # @arg $1 string Terms file TODO naming @@ -1913,8 +2032,8 @@ pl_gtf_terms2tsv() { } -submodule py_gtf_concat -# @description Python concat submodule for GTF +wrapper py_gtf_concat +# @description Python concat wrapper for GTF # Concat the process ontology outputs from the /REMcReady_lm_only folder # Probably should be translated to bash # @arg $1 string output directory name to look for txt files @@ -1928,7 +2047,7 @@ py_gtf_concat() { } -submodule r_compile_gtf +wrapper r_compile_gtf # @description Compile GTF in R # @arg $1 string gtf output directory r_compile_gtf() { @@ -1939,176 +2058,91 @@ r_compile_gtf() { } -submodule get_studies + # @description Parse study names from StudyInfo.csv files # # TODO # -# * This whole submodule should eventually be either +# * This whole wrapper should eventually be either # * Removed # * Expanded into a file that stores all project/study settings (database) # * I had to had a new line to the end of StudyInfo.csv, may break things? # -# -# # @exitcode 0 If one or more studies found # @exitcode 1 If no studies found # @set STUDIES_NUMS array Contains Exp numbers -# @arg $1 string File to read +# @arg $1 string Study info file # get_studies() { debug "Running: ${FUNCNAME[0]}" declare -ga STUDIES_NUMS=() - # Use initials from project or whoami? - # Best I can do is first two letters of username - # See TODO in markdown - initials="${USER:0:2}" - INITIALS=${initials^^} - empty_study=1 - - # Find an Exp directory that does not exist - while [[ -d $QHTCP_PROJECT_DIR/Exp$empty_study ]]; do - (( empty_study++ )) - done - - next_study_entry="$empty_study,$PROJECT_SUFFIX,NA,NA,$INITIALS" - - echo "${underline}Study Info File${nounderline}" - - if [[ ! -f $1 ]]; then - echo "Creating default $1" - echo "ExpNumb,ExpLabel,BackgroundSD,ZscoreJoinSD,AnalysisBy" > "$1" - echo "$next_study_entry" >> "$1" - next_study_entry="$((empty_study+1)),$PROJECT_SUFFIX,NA,NA,$INITIALS" - fi - - # Print current studies - cat <<-EOF - * Give each experiment labels to be used for the plots and specific files. - * Enter the desired Experiment names and order them in the way you want them to appear in the REMc heatmaps - - Current study info file: - - ${underline}$1${nounderline} - $(cat "$1") - - EOF - - # Allow user to add/edit the study info file - if ! ((YES)); then - for ((i=1; i<2; i++)); do - cat <<-EOF - Next entry suggestion: "$next_study_entry" - - Would you like to: - * (a)dd the suggested entry - * (e)dit the StudyInfo.csv file manually in nano - * (c)ontinue (default) - EOF - read -r -p "(c): " response - echo "" - [[ -z $response ]] && break - case $response in - a) - echo "Adding auto-entry suggestion to $1" - echo "$next_study_entry" >> "$1" - next_study_entry="$((empty_study+1)),$PROJECT_SUFFIX,NA,NA,$INITIALS" - i=0 - ;; - e) - debug "nano $1" - nano "$1" - ;; - c) - break - ;; - *) - err "Invalid response, please try again" - i=0 - ;; - esac - break - done - fi # Read study info file - while IFS=',' read -r col1 _; do # split on comma, get second col + while IFS=',' read -r col1 _; do # split on comma, get Exp # from 1st column STUDIES_NUMS+=("$col1") done < <(tail -n +2 "$1") # skip header [[ ${#STUDIES_NUMS[@]} -gt 0 ]] - # Initialize missing dirs - STUDIES_DIRS=() - for s in "${STUDIES_NUMS[@]}"; do - STUDY_DIR="$QHTCP_PROJECT_DIR/Exp$s" - STUDIES_DIRS+=("$STUDY_DIR") - [[ -d $STUDY_DIR ]] || mkdir "$STUDY_DIR" - # # We don't need a template anymore? - # if ! rsync --archive "$STUDY_TEMPLATE_DIR" "$STUDY_DIR"; then - # err "Could not copy $STUDY_TEMPLATE_DIR template to $STUDY_DIR" - # continue - # fi - # fi - done - unset STUDY_DIR + unset STUDY_DIR } -# submodule choose_easy_results_dir # -# # @description Chooses an EASY scans directory if the information is undefined -# # TODO Standardize EASY output, it's hard to understand -# # TODO eventually we could run this on multiple results dirs simultaneously with some refactoring -# # @exitcode 0 if successfully choose an EASY results dir -# # @set EASY_RESULTS_DIR string The working EASY output directory -# choose_easy_results_dir() { -# debug "Running: ${FUNCNAME[0]}" -# # Always backup existing output -# # This would happen if you ran the same experiment twice in one day, for instance -# [[ -d $EASY_RESULTS_DIR ]] && backup "$EASY_RESULTS_DIR" -# if [[ ! -d $EASY_RESULTS_DIR ]]; then -# debug "mkdir $EASY_RESULTS_DIR" -# mkdir "$EASY_RESULTS_DIR" -# else -# err "Could not create $EASY_RESULTS_DIR" -# return 0 -# fi -# # echo "Hit enter to use the default EASY results directory: $default_easy_results_dir" -# # if ! (( YES )); then -# # read -r -p "Or enter a custom directory name, example: $PROJECT" dirname -# # [[ -z $dirname ]] && EASY_RESULTS_DIR="$default_easy_results_dir" && return 0 -# # fi -# # ((YES)) && return 0 -# # # Let's get a little fancy -# # shopt -s nullglob -# # declare -la easy_results_dirs=( "$easy_out_dir/"*/ ) -# # shopt -u nullglob -# # # Sort the dirs -# # mapfile -t easy_results_dirs < <(printf '%s\n' "${easy_results_dirs[@]}" | sort) -# # last_index=$(( ${#easy_results_dirs} - 1 )) -# # ((YES)) && EASY_RESULTS_DIR="${easy_results_dirs[$last_index]}" && return 0 -# # echo "Multiple EASY results dirs found in $PROJECT_SCANS_DIR" -# # echo "Here is a list: " -# # for i in "${!easy_results_dirs[@]}"; do -# # printf "%d. %s\n" "$((i+1))" "${easy_results_dirs[i]}" -# # done -# # printf "%s\n" "${easy_results_dirs[@]}" -# # last_index=$(( ${#easy_results_dirs} - 1 )) -# # read -r -p "Enter the item number to select EASY results directory, default ($last_index): " response -# # [[ -z $response ]] && response=$last_index -# # response=$(( response - 1 )) # bash arrays use zero indexing -# # EASY_RESULTS_DIR="${easy_results_dirs[$response]}" -# # EASY_RESULTS_FILES=("$EASY_RESULTS_DIR/"*"/PrintResults/!!"*) -# # [[ ${#easy_results_dirs[@]} -gt 0 ]] -# } +# @description Chooses an EASY scans directory if the information is undefined +# TODO Standardize EASY output, it's hard to understand +# TODO eventually we could run this on multiple results dirs simultaneously with some refactoring +# @exitcode 0 if successfully choose anEASY results dir +# @set EASY_RESULTS_DIR string The working EASY output directory +# @arg $1 string directory containing EASY results dirs +choose_easy_results() { + debug "Running: ${FUNCNAME[0]} $*" + + shopt -s nullglob + declare -a easy_results_dirs=( "$1/"*/ ) + shopt -u nullglob + + num=${#easy_results_dirs[@]} + + if [[ $num -eq 0 ]]; then + echo "No easy results dirs found in $EASY_OUT_DIR" + echo "Run easy module first to generate results files" + return 1 + elif [[ $num -eq 1 ]]; then + EASY_RESULTS_DIR="${easy_results_dirs[0]}" + return 0 + fi + + # Sort the dirs + mapfile -t easy_results_dirs < <(printf '%s\n' "${easy_results_dirs[@]}" | sort) + + # Use latest in auto mode + if ((YES)); then + EASY_RESULTS_DIR="${easy_results_dirs[$((num-1))]}" + return 0 + fi + + for ((i=0; i for the latest easy results ($num) + EOF + read -r -p "($num): " response + [[ -z $response ]] && response="$num" + EASY_RESULTS_DIR="${easy_results_dirs[$((response-1))]}" + # EASY_RESULTS_FILES=("$EASY_RESULTS_DIR/"*"/PrintResults/!!"*) + [[ -d $EASY_RESULTS_DIR ]] +} -submodule documentation +module generate_markdown # @description Generates shdoc markdown from this script # @noargs # @internal -documentation() { +generate_markdown() { debug "Running: ${FUNCNAME[0]}" # Print markdown to stdout ((DEBUG)) && shdoc < "$SCRIPT" @@ -2126,11 +2160,11 @@ main() { debug "Running: ${FUNCNAME[0]} $*" # Libraries - declare -g JAVA="${JAVA:-$(which java 2>/dev/null)}" - declare -g PYTHON="${PYTHON:-$(which python3 2>/dev/null)}" - declare -g PERL="${PERL:-$(which perl 2>/dev/null)}" - declare -g RSCRIPT="${RSCRIPT:-$(which Rscript 2>/dev/null)}" - declare -g MATLAB="${MATLAB:-$(which matlab 2>/dev/null)}" + declare -g JAVA="${JAVA:-$(which java 2>/dev/null || echo java)}" + declare -g PYTHON="${PYTHON:-$(which python3 2>/dev/null || echo python)}" + declare -g PERL="${PERL:-$(which perl 2>/dev/null || echo perl)}" + declare -g RSCRIPT="${RSCRIPT:-$(which Rscript 2>/dev/null || echo Rscript)}" + declare -g MATLAB="${MATLAB:-$(which matlab 2>/dev/null || echo matlab)}" # Global vars SCRIPT_NAME="${BASH_SOURCE[0]##*/}" @@ -2138,7 +2172,7 @@ main() { SCRIPT_DIR=$(dirname "$SCRIPT") APPS_DIR="$SCRIPT_DIR/apps" TEMPLATES_DIR="$SCRIPT_DIR/templates" - USER="$(whoami)" + USER="${USER:-$(whoami)}" DATE="$(date +%Y%m%d)" # change in EASYconsole.m to match 'hardcode' # Find a scans directory @@ -2235,11 +2269,11 @@ main() { unset module done - # Sanitize SUBMODULES - for i in "${!SUBMODULES[@]}"; do - if ! [[ " ${ALL_SUBMODULES[*]} " =~ [[:space:]]${SUBMODULES[i]}[[:space:]] ]]; then - echo "Submodule ${SUBMODULES[$i]} not in the module list, removing" - unset "SUBMODULES[i]" "SUBMODULES[$((i+1))]" + # Sanitize wrappers + for i in "${!WRAPPERS[@]}"; do + if ! [[ " ${ALL_WRAPPERS[*]} " =~ [[:space:]]${WRAPPERS[i]}[[:space:]] ]]; then + echo "Wrapper ${WRAPPERS[$i]} not in the module list, removing" + unset "WRAPPERS[i]" "WRAPPERS[$((i+1))]" fi continue 2 # skip the arguments string done @@ -2252,32 +2286,34 @@ main() { declare -gx PROJECT_SUFFIX="${PROJECT_NAME#????????_*_}" declare -gx PROJECT_USER="${PROJECT_NAME#????????_}"; PROJECT_USER="${PROJECT_USER%%_*}" declare -gx STUDIES_ARCHIVE_FILE="$OUT_DIR/StudiesDataArchive.txt" - declare -gx QHTCP_PROJECT_DIR="$OUT_DIR/$PROJECT_NAME" + declare -gx QHTCP_RESULTS_DIR="$OUT_DIR/$PROJECT_NAME" declare -gx QHTCP_TEMPLATE_DIR="$TEMPLATES_DIR/qhtcp" - declare -gx STUDY_INFO_FILE="$QHTCP_PROJECT_DIR/StudyInfo.csv" - declare -gx EASY_OUT_DIR="$QHTCP_PROJECT_DIR/easy" + declare -gx STUDY_INFO_FILE="$QHTCP_RESULTS_DIR/StudyInfo.csv" + declare -gx EASY_OUT_DIR="$QHTCP_RESULTS_DIR/easy" + declare -gx EASY_RESULTS_DIR="$EASY_OUT_DIR/$PROJECT_PREFIX" declare -gx R_LIBS_USER=${R_LIBS_USER:-"$HOME/R/$SCRIPT_NAME"} if ((DEBUG)); then + echo "Debug:" declare -p SCANS_DIR OUT_DIR TEMPLATES_DIR APPS_DIR \ PROJECTS PROJECT_NAME \ PROJECT_SCANS_DIR PROJECT_DATE PROJECT_SUFFIX PROJECT_USER \ - STUDIES_ARCHIVE_FILE QHTCP_PROJECT_DIR QHTCP_TEMPLATE_DIR \ - STUDY_INFO_FILE + STUDIES_ARCHIVE_FILE QHTCP_RESULTS_DIR QHTCP_TEMPLATE_DIR \ + STUDY_INFO_FILE EASY_RESULTS_DIR R_LIBS_USER fi debug "Active modules: ${MODULES[*]}" - debug "Active submodules and their args: ${SUBMODULES[*]}" + debug "Active wrappers and their args: ${WRAPPERS[*]}" # Run selected modules for m in "${MODULES[@]}"; do ask "Run $m module?" && "$m" done - # Run selected submodules - for i in "${!SUBMODULES[@]}"; do - IFS=',' read -ra cmds <<< "${SUBMODULES[$((i+1))]}" # load the command args - ask "Run ${SUBMODULES[i]} submodule with args ${cmds[*]}?" && - "${SUBMODULES[i]}" "${cmds[@]}" + # Run selected wrappers + for i in "${!WRAPPERS[@]}"; do + IFS=',' read -ra cmds <<< "${WRAPPERS[$((i+1))]}" # load the command args + ask "Run ${WRAPPERS[i]} wrapper with args ${cmds[*]}?" && + "${WRAPPERS[i]}" "${cmds[@]}" continue 2 # skip the command string done @@ -2285,16 +2321,18 @@ main() { cat <<-EOF Successfully ran module(s): ${MODULES[*]} - And submodule(s): ${SUBMODULES[*]} + And wrapper(s): ${WRAPPERS[*]} On project(s): ${PROJECTS[*]} EOF - unset PROJECTS MODULE SUBMODULES EXCLUDE_MODULES + unset PROJECTS MODULES WRAPPERS EXCLUDE_MODULES } # (Safe) main loop -for ((i=1; i<2; i++)); do - main "$@" && - i=0 -done +if main "$@"; then + for ((i=1; i<2; i++)); do + main && + i=0 + done +fi exit $? diff --git a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/1.bmp b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/1.bmp deleted file mode 100644 index 21e595d7..00000000 Binary files a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/1.bmp and /dev/null differ diff --git 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b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/Times.txt deleted file mode 100644 index 69f2d611..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/Times.txt +++ /dev/null @@ -1,42 +0,0 @@ -0000 -10871 -21740 -32590 -43456 -54325 -65174 -76041 -86892 -97740 -108591 -119439 -130308 -141158 -152025 -162894 -173744 -184591 -195460 -206308 -217175 -228025 -238872 -249723 -260571 -271415 -282260 -293104 -303948 -314792 -325636 -336480 -347350 -358200 -369052 -379921 -390789 -401656 -412503 -423372 -434220 -445068 diff --git a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/Trays.txt b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/Trays.txt deleted file mode 100644 index e7965016..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/1/Trays.txt +++ /dev/null @@ -1,42 +0,0 @@ -d:\jobs\24_0719_PDR1CYC8_P6_TC\1\1.bmp,7/19/2024 2:47:05 PM 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b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/2/Times.txt deleted file mode 100644 index 07c93eb7..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/2/Times.txt +++ /dev/null @@ -1,42 +0,0 @@ -0000 -10887 -21755 -32605 -43471 -54341 -65189 -76056 -86907 -97754 -108606 -119454 -130323 -141173 -152040 -162909 -173758 -184606 -195475 -206323 -217190 -228041 -238887 -249738 -260586 -271431 -282276 -293121 -303966 -314811 -325656 -336501 -347365 -358215 -369067 -379936 -390804 -401671 -412518 -423387 -434235 -445083 diff --git a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/2/Trays.txt b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/2/Trays.txt deleted file mode 100644 index cb2b5065..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/2/Trays.txt +++ /dev/null @@ -1,42 +0,0 @@ -d:\jobs\24_0719_PDR1CYC8_P6_TC\2\1.bmp,7/19/2024 2:47:54 PM 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b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/5/Times.txt deleted file mode 100644 index 88028119..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/5/Times.txt +++ /dev/null @@ -1,42 +0,0 @@ -0000 -10944 -21811 -32662 -43528 -54397 -65246 -76113 -86964 -97811 -108662 -119510 -130380 -141230 -152097 -162966 -173815 -184662 -195532 -206380 -217247 -228097 -238944 -249795 -260642 -271488 -282334 -293180 -304026 -314872 -325718 -336564 -347421 -358272 -369124 -379993 -390861 -401728 -412575 -423443 -434292 -445140 diff --git a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/5/Trays.txt b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/5/Trays.txt deleted file mode 100644 index 584dafa5..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/5/Trays.txt +++ /dev/null @@ -1,42 +0,0 @@ -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\1.bmp,7/19/2024 2:50:15 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\2.bmp,7/19/2024 5:52:39 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\3.bmp,7/19/2024 8:53:46 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\4.bmp,7/19/2024 11:54:37 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\5.bmp,7/20/2024 2:55:43 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\6.bmp,7/20/2024 5:56:52 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\7.bmp,7/20/2024 8:57:41 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\8.bmp,7/20/2024 11:58:48 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\9.bmp,7/20/2024 2:59:39 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\10.bmp,7/20/2024 6:00:26 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\11.bmp,7/20/2024 9:01:17 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\12.bmp,7/21/2024 12:02:05 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\13.bmp,7/21/2024 3:03:15 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\14.bmp,7/21/2024 6:04:05 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\15.bmp,7/21/2024 9:05:12 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\16.bmp,7/21/2024 12:06:21 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\17.bmp,7/21/2024 3:07:10 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\18.bmp,7/21/2024 6:07:57 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\19.bmp,7/21/2024 9:09:07 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\20.bmp,7/22/2024 12:09:55 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\21.bmp,7/22/2024 3:11:02 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\22.bmp,7/22/2024 6:11:52 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\23.bmp,7/22/2024 9:12:39 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\24.bmp,7/22/2024 12:13:30 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\25.bmp,7/22/2024 3:14:17 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\26.bmp,7/22/2024 6:15:03 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\27.bmp,7/22/2024 9:15:49 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\28.bmp,7/23/2024 12:16:35 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\29.bmp,7/23/2024 3:17:21 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\30.bmp,7/23/2024 6:18:07 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\31.bmp,7/23/2024 9:18:53 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\32.bmp,7/23/2024 12:19:39 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\33.bmp,7/23/2024 3:20:36 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\34.bmp,7/23/2024 6:21:27 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\35.bmp,7/23/2024 9:22:19 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\36.bmp,7/24/2024 12:23:28 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\37.bmp,7/24/2024 3:24:36 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\38.bmp,7/24/2024 6:25:43 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\39.bmp,7/24/2024 9:26:30 AM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\40.bmp,7/24/2024 12:27:38 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\41.bmp,7/24/2024 3:28:27 PM -d:\jobs\24_0719_PDR1CYC8_P6_TC\5\42.bmp,7/24/2024 6:29:15 PM diff --git a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/InitialPoints.txt b/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/InitialPoints.txt deleted file mode 100644 index a8b017bf..00000000 --- a/workflow/templates/scans-demo/20240719_RyanMan2_PDR1CYC8_P6_TC/InitialPoints.txt +++ /dev/null @@ -1 +0,0 @@ -384,76,75,70,145 \ No newline at end of file diff --git a/workflow/templates/scans-demo/README.md b/workflow/templates/scans-demo/README.md deleted file mode 100644 index 4831a5e3..00000000 --- a/workflow/templates/scans-demo/README.md +++ /dev/null @@ -1 +0,0 @@ -This directory is intended to mimic the scans directory for demonstrative purposes.