Rollup

workflow/apps/r/CompileGTF.R

@@ -45,7 +45,7 @@ Function <- file.path(outDir, "Function", "FunctionResults.txt")
 Process <- file.path(outDir, "Process", "ProcessResults.txt")
 
 # Specify the list of input files (both CSV and TXT)
-file_list <- c(Component,Process,Function)
+file_list <- c(Component, Process, Function)
 
 # Specify the output file name
 output_file <- file.path(outDir, "GTFCombined.xlsx")

workflow/apps/r/addShiftVals.R

@@ -7,7 +7,7 @@ library(plyr)
 library(dplyr)
 library(sos)
 
-args=commandArgs(TRUE)
+args <- commandArgs(TRUE)
 
 if (length(args) >= 1) {
   finalTable <- args[1]
@@ -30,62 +30,61 @@ if (length(args) >= 3) {
 if (length(args) >= 4) {
   output <- args[4]
 } else {
-  output<- "REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
+  output <- "REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
 }
 
-
 # Read in the REMc finalTable data
-X = data.frame(read.csv(file=finalTable,header=TRUE,stringsAsFactors = FALSE))
+X <- data.frame(read.csv(file = finalTable, header = TRUE, stringsAsFactors = FALSE))
 # Read in the shift data From ../JoinInteractions
-Y = data.frame(read.csv(file=shiftFile,header=TRUE,stringsAsFactors = FALSE))
-Labels <- read.delim(studyInfo,skip=0,as.is=T,row.names=1,strip.white=TRUE)
+Y <- data.frame(read.csv(file = shiftFile, header = TRUE, stringsAsFactors = FALSE))
+Labels <- read.delim(studyInfo, skip = 0, as.is = TRUE, row.names = 1, strip.white = TRUE)
 
 # Determine the number of cols - needed to create the correct number of new cols
-Xcolnum <- length(X[1,])
-ADDnum <- Xcolnum + length(Y[1,]) - 2
+Xcolnum <- length(X[1, ])
+ADDnum <- Xcolnum + length(Y[1, ]) - 2
 
 # Create new columns filled with NAs to be filled with data
-Xtemp= X
-Xtemp[,(Xcolnum+1):ADDnum] <- NA
+Xtemp <- X
+Xtemp[, (Xcolnum + 1):ADDnum] <- NA
 
 # Match the orf names in each row to a orf name in the shift data file and then add the shift data to the finalTable file
-shiftTbl <-as.data.frame(matrix(nrow=1,ncol=length(Y)-2)) #the df shiftTbl must be initialized before for loop
+shiftTbl < - as.data.frame(matrix(nrow = 1, ncol = length(Y) - 2)) #the df shiftTbl must be initialized before for loop
 
-for(i in 1:length(X[,1])){
-  Shiftrownum = match(X[i,2],Y[,1])
-  shiftTbl[i,]= Y[Shiftrownum,3:length(Y[1,])]
-  Xtemp[i,(Xcolnum+1):ADDnum] <- Y[Shiftrownum,3:length(Y[1,])]
+for (i in 1:length(X[, 1])) {
+  Shiftrownum <- match(X[i, 2], Y[, 1])
+  shiftTbl[i, ] <- Y[Shiftrownum, 3:length(Y[1, ])]
+  Xtemp[i, (Xcolnum + 1):ADDnum] <- Y[Shiftrownum, 3:length(Y[1, ])]
 }
-headerX= colnames(Xtemp)
-headerY= colnames(Y)
-shfHdr= headerY[3:length(headerY)]
-combTbl<- X[,1:3]
-lmTbl= select(Xtemp,  contains('Z_lm'))  #X[,(4:Xcolnum-2)]
-shiftTbl<- select(Xtemp,  contains('V'))
-clustTbl<- select(Xtemp, contains('cluster.'))
+headerX <- colnames(Xtemp)
+headerY <- colnames(Y)
+shfHdr <- headerY[3:length(headerY)]
+combTbl <- X[, 1:3]
+lmTbl <- select(Xtemp,  contains("Z_lm"))  #X[,(4:Xcolnum-2)]
+shiftTbl <- select(Xtemp,  contains("V"))
+clustTbl <- select(Xtemp, contains("cluster."))
 
 # Give the new column names the same names as in the shift file
-Xcols = colnames(X)
-Ycols = colnames(Y)[3:length(Y[1,])]
-newCols = c(Xcols[1:Xcolnum],Ycols)
+Xcols <- colnames(X)
+Ycols <- colnames(Y)[3:length(Y[1, ])]
+newCols <- c(Xcols[1:Xcolnum], Ycols)
 
 # Reorder columns for generating heatmaps
-combI= combTbl  #Starting Template orf, Genename columns
-headersRemc<-newCols  #colnames(X)
-newHeaders= newCols[1:3]
-lmHdr= colnames(lmTbl)   #newCols[4:(length(Xcols)-2)]
-clstHdr= colnames(clustTbl)  #select(newCols, contains('cluster.'))  #newCols[3+length(lmHdr):2]
+combI <- combTbl  #Starting Template orf, Genename columns
+headersRemc <- newCols  #colnames(X)
+newHeaders <- newCols[1:3]
+lmHdr <- colnames(lmTbl)   #newCols[4:(length(Xcols)-2)]
+clstHdr <- colnames(clustTbl)  #select(newCols, contains('cluster.'))  #newCols[3+length(lmHdr):2]
 
-intLvHdr= vector()
+intLvHdr <- vector()
 #Reorder columns to produce an interleaved set of Z_lm and Shift data for all the cpps.
-for(i in 1:(length(shiftTbl[1,]))){
-  combI=cbind.data.frame(combI, shiftTbl[i])
-  combI=cbind.data.frame(combI, lmTbl[i])
-  intLvHdrx= c(shfHdr[i],lmHdr[i] )
-  intLvHdr= c(intLvHdr,intLvHdrx)
+for (i in 1:(length(shiftTbl[1, ]))) {
+  combI <- cbind.data.frame(combI, shiftTbl[i])
+  combI <- cbind.data.frame(combI, lmTbl[i])
+  intLvHdrx <- c(shfHdr[i], lmHdr[i])
+  intLvHdr <- c(intLvHdr, intLvHdrx)
 }
 
-combIHdr= c(colnames(combTbl),intLvHdr,clstHdr)
-combI=cbind.data.frame(combI, clustTbl)
-colnames(combI)= combIHdr
-write.csv(combI,file=output, row.names=FALSE)
+combIHdr <- c(colnames(combTbl), intLvHdr, clstHdr)
+combI <- cbind.data.frame(combI, clustTbl)
+colnames(combI) <- combIHdr
+write.csv(combI, file = output, row.names = FALSE)

workflow/apps/r/createHeatMaps.R

@@ -9,18 +9,18 @@ args <- commandArgs(TRUE)
 
 # Set output dir
 if (length(args) >= 1) {
-  input_finalTable <- args[1]
+  input_finalTable <- file.path(args[1])
 } else {
   input_finalTable <- "/REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
 }
 
 if (length(args) >= 2) {
-  outDir <- args[2]
+  outDir <- file.path(args[2])
 } else {
   outDir <- "/REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
 }
 
-hmapfile <- data.frame(read.csv(file=input_finalTable,header=TRUE,sep=",",stringsAsFactors = FALSE))
+hmapfile <- data.frame(read.csv(file = input_finalTable, header = TRUE, sep = ",", stringsAsFactors = FALSE))
 # set NAs to NA
 hmapfile[hmapfile == -100] <- NA
 hmapfile[hmapfile == 100] <- NA
@@ -28,19 +28,20 @@ hmapfile[hmapfile == 0.001] <- NA
 hmapfile[hmapfile == -0.001] <- NA
 
 # select the number of rows based on the number of genes
-num_total_genes <- length(hmapfile[,1])
+num_total_genes <- length(hmapfile[, 1])
 
 # Break out the cluster names so each part of the cluster origin can be accessed
-# line below removed because it adds to many genes to clusters when going past 1-0-10 since it cannot differentiate between 1-0-1 and 1-0-10 when using grepl.
+# line below removed because it adds to many genes to clusters when going past 1-0-10
+# since it cannot differentiate between 1-0-1 and 1-0-10 when using grepl.
 # hmapfile$cluster.origin = gsub(" ","",x=hmapfile$cluster.origin)
 
-hmapfile$cluster.origin = gsub(";"," ;",x=hmapfile$cluster.origin)
-hmapfile$cluster.origin = strsplit(hmapfile$cluster.origin,';')
+hmapfile$cluster.origin <- gsub(";", " ;", x = hmapfile$cluster.origin)
+hmapfile$cluster.origin <- strsplit(hmapfile$cluster.origin, ";")
 #use tail(x,n) for accessing the outward most cluster
 
 clust_rounds <- 0
-for(i in 1:num_total_genes){
-  if(length(hmapfile$cluster.origin[[i]]) > clust_rounds){
+for (i in 1:num_total_genes) {
+  if (length(hmapfile$cluster.origin[[i]]) > clust_rounds) {
     clust_rounds <- length(hmapfile$cluster.origin[[i]])
   }
 }
@@ -49,12 +50,13 @@ unique_clusts <- unique(hmapfile$cluster.origin[1:num_total_genes])
 unique_clusts <- unique_clusts[unique_clusts != " "]
 
 # Select only the unique cluster names
-unique_clusts <- sort(unique(unlist(unique_clusts,use.names= FALSE)),decreasing=FALSE)
+unique_clusts <- sort(unique(unlist(unique_clusts, use.names = FALSE)), decreasing = FALSE)
 num_unique_clusts <- length(unique_clusts)
 
 # Base the color key on a statistical analysis of the L and K data
 # need to create "breaks" to set the color key, need to have 12 different breaks (for 11 colors)
-# scale() will calculate the mean and standard deviation of the entire vector, then "scale" each element by those values by subtracting the mean and dividing by the sd.
+# scale() will calculate the mean and standard deviation of the entire vector,
+# then "scale" each element by those values by subtracting the mean and dividing by the sd.
 # hmapfile[,4:(length(hmapfile[1,]) - 2)] <- scale(hmapfile[,4:(length(hmapfile[1,]) - 2)])
 # change so that the L data is multiplied to be on the same scale as the K data
 
@@ -65,12 +67,12 @@ L_MAX <- 0
 KcolumnValues <- vector()
 LcolumnValues <- vector()
 
-for(i in 4:(length(hmapfile[1,]) - 2)){
-  if(grepl("_Z_lm_K",colnames(hmapfile)[i],fixed=TRUE) == TRUE){
-    KcolumnValues <- append(KcolumnValues,i)
+for (i in 4:(length(hmapfile[1, ]) - 2)){
+  if (grepl("_Z_lm_K", colnames(hmapfile)[i], fixed = TRUE) == TRUE) {
+    KcolumnValues <- append(KcolumnValues, i)
   }
-  if(grepl("_Z_lm_L",colnames(hmapfile)[i],fixed=TRUE) == TRUE){
-    LcolumnValues <- append(LcolumnValues,i)
+  if (grepl("_Z_lm_L", colnames(hmapfile)[i], fixed = TRUE) == TRUE) {
+    LcolumnValues <- append(LcolumnValues, i)
   }
 }
 
@@ -101,38 +103,41 @@ print(KEY_MIN)
 print(L_MAX)
 # print(L_Multiplier)
 
-colormapbreaks <- c(KEY_MIN,KEY_MIN*(5/6),KEY_MIN*(4/6),KEY_MIN*(3/6),KEY_MIN*(2/6),KEY_MIN*(1/6),KEY_MAX*(1/6),KEY_MAX*(2/6),KEY_MAX*(3/6),KEY_MAX*(4/6),KEY_MAX*(5/6),KEY_MAX)
+colormapbreaks <- c(KEY_MIN, KEY_MIN * (5 / 6), KEY_MIN * (4 / 6), KEY_MIN * (3 / 6), KEY_MIN * (2 / 6),
+  KEY_MIN * (1 / 6), KEY_MAX * (1 / 6), KEY_MAX * (2 / 6), KEY_MAX * (3 / 6), KEY_MAX * (4 / 6), KEY_MAX * (5 / 6), KEY_MAX)
 # print(colormapbreaks)
 
 # Probably should give a way to detect shift in case that is is not in the first row... (maybe just grepl for the whole column name?)
-# However since also using this to amend the first part. Could possibly identify all the ones that contain the word shift and then create an object containing just those numbers
-# then could just use these values and create spaces only between interaction values - possibly could get rid of redundant shift values if we don't want to view these
+# However since also using this to amend the first part.
+# Could possibly identify all the ones that contain the word shift and then create an object containing just those numbers
+# then could just use these values and create spaces only between interaction values
+# possibly could get rid of redundant shift values if we don't want to view these
 # could we pool all the shift data/average it?
-if(grepl("Shift",colnames(hmapfile)[4],fixed=TRUE) == TRUE){
-  even_columns <- seq(from= 2, to= (length(hmapfile[1,]) - 7),by=2)
-  #ev_repeat = rep("white",length(even_columns))
-  #ev_repeat = rep("red",(length(hmapfile[1,]) - 5))
-  #middle_col <- (length(hmapfile[1,]) - 5)/2
-  #ev_repeat[(middle_col/2)] <- "black"
-  #print(ev_repeat)
+if (grepl("Shift", colnames(hmapfile)[4], fixed = TRUE) == TRUE) {
+  even_columns <- seq(from = 2, to = (length(hmapfile[1, ]) - 7), by = 2)
+  # ev_repeat = rep("white",length(even_columns))
+  # ev_repeat = rep("red",(length(hmapfile[1,]) - 5))
+  # middle_col <- (length(hmapfile[1,]) - 5)/2
+  # ev_repeat[(middle_col/2)] <- "black"
+  # print(ev_repeat)
 }
 
-if(grepl("Shift",colnames(hmapfile)[4],fixed=TRUE) == FALSE){
-  even_columns <- seq(from= 2, to= (length(hmapfile[1,]) - 7),by=1)
+if (grepl("Shift", colnames(hmapfile)[4], fixed = TRUE) == FALSE) {
+  even_columns <- seq(from = 2, to = (length(hmapfile[1, ]) - 7), by = 1)
   print("NO SHIFT VALS FOUND")
 }
 
-#FOR THIS SCRIPT ONLY (rap tem hu script)
-#even_columns <- c(2,5,7,10,12,15,17)
+# FOR THIS SCRIPT ONLY (rap tem hu script)
+# even_columns <- c(2,5,7,10,12,15,17)
 
-#m <- 0
-colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1,]) - 2)])
-#print(colnames_edit)
-for(i in 1:length(colnames_edit)){
-  if(grepl("Shift",colnames_edit[i],fixed=TRUE) == TRUE){
+# m <- 0
+colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1, ]) - 2)])
+# print(colnames_edit)
+for (i in 1:length(colnames_edit)) {
+  if (grepl("Shift", colnames_edit[i], fixed = TRUE) == TRUE) {
     colnames_edit[i] <- ""
-    colnames_edit[i+1] <- gsub(pattern = "_Z_lm_",replacement = " ",x = colnames_edit[i+1])
-    try(colnames_edit[i+1] <- gsub(pattern = "_",replacement = " ",x = colnames_edit[i+1]))
+    colnames_edit[i + 1] <- gsub(pattern = "_Z_lm_", replacement = " ", x = colnames_edit[i + 1])
+    try(colnames_edit[i + 1] <- gsub(pattern = "_", replacement = " ", x = colnames_edit[i + 1]))
     
     # INT_store <- strsplit(colnames_edit[i+1], "Z_lm")
     # print(length(unlist(INT_store)))
@@ -140,7 +145,7 @@ for(i in 1:length(colnames_edit)){
     #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[3],sep=" ")
     # }
     # if(length(unlist(INT_store)) == 3){
-    # 
+    #
     #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],sep=" ")
     # }
     # if(length(unlist(INT_store)) == 5){
@@ -149,140 +154,152 @@ for(i in 1:length(colnames_edit)){
     # if(length(unlist(INT_store)) == 6){
     #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[6],sep=" ")
     # }
-
   }
 }
 print(colnames_edit)
-#break()
-#colnames_edit[5] <- "TEM HLEG K"
-#colnames_edit[10] <- "TEM HL K"
-#colnames_edit[15] <- "TEM HLEG L"
-#colnames_edit[20] <- "TEM HL L"
-
+# break()
+# colnames_edit[5] <- "TEM HLEG K"
+# colnames_edit[10] <- "TEM HL K"
+# colnames_edit[15] <- "TEM HLEG L"
+# colnames_edit[20] <- "TEM HL L"
 
 # Create the heatmaps
-for(i in 1:num_unique_clusts){
+for (i in 1:num_unique_clusts) {
   cluster <- unique_clusts[i]
-  cluster_data <- subset(hmapfile,grepl(cluster,cluster.origin))
-  cluster_length <- length(cluster_data[,1])
-  if(cluster_length != 1){
-    X0 <- as.matrix(cluster_data[,4:(length(hmapfile[1,]) - 2)])
-    if(cluster_length >= 2001){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=20,width=15)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
-                breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor= "white", offsetCol = 0.1,
-                #zlim=c(-132,132), 
-                xlab = "Type of Media", ylab = "Gene Name",
-                #cellnote = round(X0,digits=0), notecex = 0.1, key=TRUE,
-                keysize=0.7, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster, 
-                #ColSideColors=ev_repeat,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
-      #abline(v=0.5467,col="black")
+  cluster_data <- subset(hmapfile, grepl(cluster, cluster.origin))
+  cluster_length <- length(cluster_data[, 1])
+  if (cluster_length != 1) {
+    X0 <- as.matrix(cluster_data[, 4:(length(hmapfile[1, ]) - 2)])
+    if (cluster_length >= 2001) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, height = 20, width = 15)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        # zlim=c(-132, 132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        # cellnote = round(X0,digits = 0), notecex = 0.1, key = TRUE,
+        keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        # ColSideColors=ev_repeat,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
+      # abline(v=0.5467,col="black")
       dev.off()
     }
-    if(cluster_length >= 201 && cluster_length <= 2000){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=15,width=12)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
-                breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
-                #zlim=c(-132,132), 
-                xlab = "Type of Media", ylab = "Gene Name",
-                cellnote = round(X0,digits=0), notecex = 0.1, key=TRUE,
-                keysize=0.7, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
-      #abline(v=0.5316,col="black")
+    if (cluster_length >= 201 && cluster_length <= 2000) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, height = 15, width = 12)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        # zlim=c(-132,132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        cellnote = round(X0, digits = 0), notecex = 0.1, key = TRUE,
+        keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
+      # abline(v=0.5316,col="black")
       dev.off()
     }
-    if(cluster_length >= 150 && cluster_length <= 200){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=12,width=12)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
-                breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
-                #zlim=c(-132,132), 
-                xlab = "Type of Media", ylab = "Gene Name",
-                cellnote = round(X0,digits=0), notecex = 0.2, key=TRUE,
-                keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
+    if (cluster_length >= 150 && cluster_length <= 200) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, height = 12, width = 12)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        # zlim=c(-132,132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        cellnote = round(X0, digits = 0), notecex = 0.2, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
       dev.off()
     }
-    if(cluster_length >= 101 && cluster_length <= 149){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,mypath,height=12,width=12)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.2, scale = "none",
-                breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
-                #zlim=c(-132,132), 
-                xlab = "Type of Media", ylab = "Gene Name",
-                cellnote = round(X0,digits=0), notecex = 0.3, key=TRUE,
-                keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
+    if (cluster_length >= 101 && cluster_length <= 149) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, mypath, height = 12, width = 12)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.2, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        #zlim=c(-132,132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
       dev.off()
     }
-    if(cluster_length >= 60 && cluster_length <= 100){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=12,width=12)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.4, scale = "none",
-                breaks=colormapbreaks,symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
-                #zlim=c(-132,132),
-                xlab = "Type of Media", ylab = "Gene Name",
-                cellnote = round(X0,digits=0), notecex = 0.3, key=TRUE,
-                keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
+    if (cluster_length >= 60 && cluster_length <= 100) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, height = 12, width = 12)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.4, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        # zlim=c(-132,132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
       dev.off()
     }
-    if(cluster_length <= 59 && cluster_length >= 30){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=9,width=12)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.6, scale = "none",
-                breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
-                #zlim=c(-132,132), 
-                xlab = "Type of Media", ylab = "Gene Name",
-                cellnote = round(X0,digits=0), notecex = 0.4, key=TRUE,
-                keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
+    if (cluster_length <= 59 && cluster_length >= 30) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, height = 9, width = 12)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.6, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        # zlim=c(-132,132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
       dev.off()
     }
-    if(cluster_length <= 29){
-      mypath = file.path(outDir,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=7,width=12)
-      heatmap.2(x=X0,
-                Rowv=TRUE, Colv=NA, 
-                distfun = dist, hclustfun = hclust,
-                dendrogram = "row", cexCol = 0.8, cexRow = 0.9, scale = "none",
-                breaks=colormapbreaks, symbreaks=FALSE, colsep = even_columns, sepcolor="white", offsetCol = 0.1,
-                #zlim=c(-132,132), 
-                xlab = "Type of Media", ylab = "Gene Name",
-                cellnote = round(X0,digits=0), notecex = 0.4, key=TRUE,
-                keysize=1, trace="none", density.info=c("none"), margins=c(10, 8),
-                na.color="red", col=brewer.pal(11,"PuOr"),
-                main=cluster,
-                labRow=as.character(cluster_data$Gene), labCol=colnames_edit)
+    if (cluster_length <= 29) {
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      pdf(file = mypath, height = 7, width = 12)
+      heatmap.2(
+        x = X0,
+        Rowv = TRUE, Colv = NA,
+        distfun = dist, hclustfun = hclust,
+        dendrogram = "row", cexCol = 0.8, cexRow = 0.9, scale = "none",
+        breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
+        # zlim=c(-132,132),
+        xlab = "Type of Media", ylab = "Gene Name",
+        cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit
+      )
       dev.off()
     }
   }
-  #print(paste("FINISHED", "CLUSTER",cluster,sep=" "))
+  # print(paste("FINISHED", "CLUSTER",cluster,sep=" "))
 }

workflow/apps/r/interactions.R

@@ -20,25 +20,25 @@ library(htmlwidgets)
 
 # Parse arguments
 args <- commandArgs(TRUE)
-inputFile <- args[1]
+inputFile <- file.path(args[1])
 
 # Set output dir
 if (length(args) >= 2) {
-  outDir <- args[2]
+  outDir <- file.path(args[2])
 } else {
   outDir <- "/ZScores/" # for legacy workflow
 }
 
 # Set StudyInfo file path
 if (length(args) >= 3) {
-  studyInfo <- args[3]
+  studyInfo <- file.path(args[3])
 } else {
   studyInfo <- "../Code/StudyInfo.csv" # for legacy workflow
 }
 
 # Set SGDgeneList file path
 if (length(args) >= 4) {
-  SGDgeneList <- args[4]
+  SGDgeneList <- file.path(args[4])
 } else {
   SGDgeneList <- "../Code/SGD_features.tab" # for legacy workflow
 }
@@ -54,7 +54,7 @@ if (length(args) >= 5) {
 }
 delBGFactor <- as.numeric(delBGFactor)
 if (is.na(delBGFactor)) {
-  delBGFactor <- 3 # Recommended by Sean
+  delBGFactor <- 3 # recommended by Sean
 }
 print(paste("The Standard Deviation Value is:", delBGFactor))
 
@@ -68,21 +68,25 @@ if (length(args) >= 6) {
 }
 expNumber <- as.numeric(expNumber)
 
-outDir_QC <- paste(outDir, "QC/", sep = "")
+outDir_QC <- file.path(outDir, "QC")
 
 if (!file.exists(outDir)) {
-  dir.create(file.path(outDir))
+  dir.create(outDir)
 }
 
 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
-# write to Labels to studyInfo.txt for future reference
-Labels <- read.csv(file = studyInfo, stringsAsFactors = FALSE)  # sep = ","
-Labels[expNumber, 3] <- delBGFactor
-write.csv(Labels, file = studyInfo, row.names = FALSE)
+options(width = 1000)
+ls.str()
+
+# Write delBJFactor to the StudyInfo file
+# TODO we probably shouldn't be doing this, need one source of truth
+# TODO disabling this for now
+# 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
 
@@ -366,19 +370,19 @@ Raw_l_vs_K_beforeQC <-
   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)
+pdf(file.path(outDir_QC, "Raw_L_vs_K_beforeQC.pdf"), width = 12, height = 8)
 Raw_l_vs_K_beforeQC
 
 dev.off()
 
 pgg <- ggplotly(Raw_l_vs_K_beforeQC)
-plotly_path <- paste(outDir_QC, "Raw_L_vs_K_beforeQC.html", sep = "")
+plotly_path <- file.path(outDir_QC, "Raw_L_vs_K_beforeQC.html")
 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 = " "))
+sprintf("Delta_Background_Tolerance is %f", Delta_Background_Tolerance)
 
 Plate_Analysis_Delta_Backgrd <-
   ggplot(X, aes(Scan, Delta_Backgrd, color = as.factor(Conc_Num))) +
@@ -417,13 +421,13 @@ X_Delta_Backgrd_above_Tolerance_L_vs_K <-
   ) +
   theme_publication_legend_right()
 
-pdf(paste(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf", sep = ""), width = 12, height = 8)
+pdf(file.path(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.pdf"), width = 12, height = 8)
 X_Delta_Backgrd_above_Tolerance_L_vs_K
 
 dev.off()
 
 pgg <- ggplotly(X_Delta_Backgrd_above_Tolerance_L_vs_K)
-plotly_path <- paste(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html", sep = "")
+plotly_path <- file.path(outDir_QC, "Raw_L_vs_K_for_strains_above_deltabackgrd_threshold.html")
 saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
 
 # Frequency plot for all data vs. the delta_background
@@ -434,7 +438,7 @@ DeltaBackground_Frequency_Plot <- ggplot(X, aes(Delta_Backgrd, color = as.factor
 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.path(outDir_QC, "Frequency_Delta_Background.pdf"), width = 12, height = 8)
 print(DeltaBackground_Frequency_Plot)
 print(DeltaBackground_Bar_Plot)
 dev.off()
@@ -568,7 +572,7 @@ Plate_Analysis_Delta_Backgrd_Box_afterQC <-
   theme_publication()
 
 # Print the plate analysis data before and after QC
-pdf(file = paste(outDir_QC, "Plate_Analysis.pdf", sep = ""), width = 14, height = 9)
+pdf(file.path(outDir_QC, "Plate_Analysis.pdf"), width = 14, height = 9)
 Plate_Analysis_L
 Plate_Analysis_L_afterQC
 Plate_Analysis_K
@@ -582,7 +586,7 @@ 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)
+pdf(file.path(outDir_QC, "Plate_Analysis_Boxplots.pdf"), width = 18, height = 9)
 Plate_Analysis_L_Box
 Plate_Analysis_L_Box_afterQC
 Plate_Analysis_K_Box
@@ -713,7 +717,7 @@ Plate_Analysis_Delta_Backgrd_Box_afterQC_Z <-
   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)
+pdf(file.path(outDir_QC, "Plate_Analysis_noZeros.pdf"), width = 14, height = 9)
 Plate_Analysis_L_afterQC_Z
 Plate_Analysis_K_afterQC_Z
 Plate_Analysis_r_afterQC_Z
@@ -722,7 +726,7 @@ 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)
+pdf(file.path(outDir_QC, "Plate_Analysis_noZeros_Boxplots.pdf"), width = 18, height = 9)
 Plate_Analysis_L_Box_afterQC_Z
 Plate_Analysis_K_Box_afterQC_Z
 Plate_Analysis_r_Box_afterQC_Z
@@ -768,7 +772,7 @@ X_stats_ALL <- ddply(
 )
 
 # print(X_stats_ALL_L)
-write.csv(X_stats_ALL, file = paste(outDir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)
+write.csv(X_stats_ALL, file.path(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
@@ -885,7 +889,7 @@ for (s in Background_Strains) {
     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.path(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
@@ -964,7 +968,7 @@ for (s in Background_Strains) {
 
   write.csv(
     X_stats_BY_L_within_2SD_K,
-    file = paste(outDir_QC, "Max_Observed_L_Vals_for_spots_within_2SD_K.csv", sep = ""),
+    file.path(outDir_QC, "Max_Observed_L_Vals_for_spots_within_2SD_K.csv"),
     row.names = FALSE
   )
   
@@ -991,13 +995,13 @@ for (s in Background_Strains) {
     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(file.path(outDir_QC, "Raw_L_vs_K_for_strains_2SD_outside_mean_K.pdf"), 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 = "")
+  plotly_path <- file.path(outDir_QC, "RawL_vs_K_for_strains_outside_2SD_K.html")
   saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
 
   Outside_2SD_K_delta_background_vs_K <-
@@ -1006,13 +1010,13 @@ for (s in Background_Strains) {
     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)
+  pdf(file.path(outDir_QC, "DeltaBackground_vs_K_for_strains_2SD_outside_mean_K.pdf"), 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 = "")
+  plotly_path <- file.path(outDir_QC, "DeltaBackground_vs_K_for_strains_outside_2SD_K.html")
   saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
 
   # Get the background strain mean values at the no drug conc to calculate shift
@@ -1046,7 +1050,7 @@ for (s in Background_Strains) {
     X2_temp <- X2[X2$Conc_Num == Concentration, ]
     if (Concentration == 0) {
       X2_new <- X2_temp
-      print(paste("Check loop order, conc =", Concentration, sep = " "))
+      sprintf("Check loop order, conc = %f", Concentration)
     }
     if (Concentration > 0) {
       try(X2_temp[X2_temp$l == 0 & !is.na(X2_temp$l), ]$l <- X_stats_BY_L_within_2SD_K$max[i])
@@ -1055,7 +1059,7 @@ for (s in Background_Strains) {
       # X2_temp[X2_temp$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 = " "))
+      sprintf("Check loop order, conc = %f", Concentration)
       X2_new <- rbind(X2_new, X2_temp)
     }
   }
@@ -1075,13 +1079,13 @@ for (s in Background_Strains) {
     X2_RF_temp <- X2_RF[X2_RF$Conc_Num == Concentration, ]
     if (Concentration == 0) {
       X2_RF_new <- X2_RF_temp
-      print(paste("Check loop order, conc =", Concentration, sep = " "))
+      sprintf("Check loop order, conc = %f", Concentration)
     }
     if (Concentration > 0) {
       try(X2_RF_temp[X2_RF_temp$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 = " "))
+      sprintf("If error, refs have no L values outside theoretical max L, for REFs, conc = %f", Concentration)
       X2_RF_new <- rbind(X2_RF_new, X2_RF_temp)
     }
   }
@@ -1446,7 +1450,7 @@ for (s in Background_Strains) {
   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)
+  write.csv(InteractionScores_RF, file.path(outDir, "RF_ZScores_Interaction.csv"), row.names = FALSE)
   
   for (i in 1:num_genes_RF) {
     Gene_Sel <- unique(InteractionScores_RF$OrfRep)[i]
@@ -1548,7 +1552,7 @@ for (s in Background_Strains) {
     }
   }
   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, file.path(outDir, "RF_ZScore_Calculations.csv"), row.names = FALSE)
   
   # Part 5 - Get Zscores for Gene deletion strains
   
@@ -1904,7 +1908,7 @@ for (s in Background_Strains) {
   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)
+  write.csv(InteractionScores, file.path(outDir, "ZScores_Interaction.csv"), row.names = FALSE)
   
   InteractionScores_deletion_enhancers_L <-
     InteractionScores[InteractionScores$Avg_Zscore_L >= 2, ]
@@ -1957,25 +1961,25 @@ for (s in Background_Strains) {
     InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K[
       !is.na(InteractionScores_deletion_enhancers_Avg_Zscore_Suppressors_lm_K$OrfRep), ]
   write.csv(InteractionScores_deletion_enhancers_L,
-    paste(outDir, "ZScores_Interaction_DeletionEnhancers_L.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_L.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_enhancers_K,
-    paste(outDir, "ZScores_Interaction_DeletionEnhancers_K.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_K.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_suppressors_L,
-    paste(outDir, "ZScores_Interaction_DeletionSuppressors_L.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_L.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_suppressors_K,
-    paste(outDir, "ZScores_Interaction_DeletionSuppressors_K.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_K.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_enhancers_and_Suppressors_L,
-    paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_enhancers_and_Suppressors_K,
-    paste(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K.csv"), 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)
+    file.path(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_L.csv"), 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)
+    file.path(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_L.csv"), 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)
+    file.path(outDir, "ZScores_Interaction_Suppressors_and_lm_Enhancers_K.csv"), 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)
+    file.path(outDir, "ZScores_Interaction_Enhancers_and_lm_Suppressors_K.csv"), row.names = FALSE)
   
   # Get enhancers and suppressors for linear regression
   InteractionScores_deletion_enhancers_L_lm <-
@@ -2010,19 +2014,19 @@ for (s in Background_Strains) {
       !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)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_L_lm.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_enhancers_K_lm,
-    paste(outDir, "ZScores_Interaction_DeletionEnhancers_K_lm.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_K_lm.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_suppressors_L_lm,
-    paste(outDir, "ZScores_Interaction_DeletionSuppressors_L_lm.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_L_lm.csv"), row.names = FALSE)
   write.csv(InteractionScores_deletion_suppressors_K_lm,
-    paste(outDir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv", sep = ""), row.names = FALSE)
+    file.path(outDir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv"), 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)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_L_lm.csv"), 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)
+    file.path(outDir, "ZScores_Interaction_DeletionEnhancers_and_Suppressors_K_lm.csv"), row.names = FALSE)
+  # write.csv(Labels, studyInfo, row.names = FALSE)
+  # write.table(Labels, file.path("../Code/StudyInfo.txt"), sep = "\t", row.names = FALSE)
   
   for (i in 1:num_genes) {
     Gene_Sel <- unique(InteractionScores$OrfRep)[i]
@@ -2113,11 +2117,11 @@ for (s in Background_Strains) {
     }
   }
   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, file.path(outDir, "ZScore_Calculations.csv"), row.names = FALSE)
   
   Blank <- ggplot(X2_RF) + geom_blank()
   
-  pdf(paste(outDir, "InteractionPlots.pdf", sep = ""), width = 16, height = 16, onefile = TRUE)
+  pdf(file.path(outDir, "InteractionPlots.pdf"), width = 16, height = 16, onefile = TRUE)
   
   X_stats_X2_RF <- ddply(
     X2_RF,
@@ -2344,7 +2348,7 @@ for (s in Background_Strains) {
 
   dev.off()
 
-  pdf(paste(outDir, "RF_InteractionPlots.pdf", sep = ""), width = 16, height = 16, onefile = TRUE)
+  pdf(file.path(outDir, "RF_InteractionPlots.pdf"), width = 16, height = 16, onefile = TRUE)
 
   X_stats_X2_RF <- ddply(
     X2_RF,
@@ -2732,7 +2736,7 @@ for (s in Background_Strains) {
     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)
+  pdf(file.path(outDir, "RankPlots.pdf"), width = 18, height = 12, onefile = TRUE)
   
   grid.arrange(
     Rank_L_1SD,
@@ -2866,7 +2870,7 @@ for (s in Background_Strains) {
     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)
+  pdf(file.path(outDir, "RankPlots_lm.pdf"), width = 18, height = 12, onefile = TRUE)
   
   grid.arrange(
     Rank_L_1SD_lm,
@@ -2916,7 +2920,7 @@ for (s in Background_Strains) {
   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(file.path(outDir, "Avg_Zscore_vs_lm_NA_rm.pdf"), width = 16, height = 12, onefile = TRUE)
   
   print(ggplot(X_NArm, aes(Avg_Zscore_L, Z_lm_L)) +
     geom_point(aes(color = Overlap), shape = 3) +
@@ -2955,7 +2959,7 @@ for (s in Background_Strains) {
     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)
-  plotly_path <- paste(outDir, "Avg_Zscore_vs_lm_NA_rm.html", sep = "")
+  plotly_path <- file.path(outDir, "Avg_Zscore_vs_lm_NA_rm.html")
   saveWidget(pgg, file = plotly_path, selfcontained = TRUE)
   
   X_NArm$L_Rank <- rank(X_NArm$Avg_Zscore_L)
@@ -2978,7 +2982,7 @@ for (s in Background_Strains) {
   AUC_lm2 <- summary(get_lm_AUC2)
   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(file.path(outDir, "Avg_Zscore_vs_lm_ranked_NA_rm.pdf"), width = 16, height = 12, onefile = TRUE)
   
   print(
     ggplot(X_NArm, aes(L_Rank, L_Rank_lm)) +
@@ -3114,7 +3118,7 @@ for (s in Background_Strains) {
     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)
+  pdf(file.path(outDir, "RankPlots_naRM.pdf"), width = 18, height = 12, onefile = TRUE)
   
   grid.arrange(
     Rank_L_1SD,
@@ -3234,7 +3238,7 @@ for (s in Background_Strains) {
     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)
+  pdf(file.path(outDir, "RankPlots_lm_naRM.pdf"), width = 18, height = 12, onefile = TRUE)
   
   grid.arrange(
     Rank_L_1SD_lm,
@@ -3273,7 +3277,7 @@ L_lm_5 <- summary(get_lm_5)
 get_lm_6 <- lm(X_NArm$Z_lm_AUC ~ X_NArm$Z_lm_r)
 L_lm_6 <- summary(get_lm_6)
 
-pdf(file = paste(outDir, "Correlation_CPPs.pdf", sep = ""), width = 10, height = 7, onefile = TRUE)
+pdf(file.path(outDir, "Correlation_CPPs.pdf"), width = 10, height = 7, onefile = TRUE)
 
 ggplot(X_NArm, aes(Z_lm_L, Z_lm_K)) +
   geom_point(shape = 3, color = "gray70") +
@@ -3482,5 +3486,5 @@ ggplot(X_NArm, aes(Z_lm_r, Z_lm_AUC)) +
 
 dev.off()
 
-# write.csv(Labels, file = paste("../Code/Parameters.csv"), row.names = FALSE)
+# write.csv(Labels, file.path("../Code/Parameters.csv"), row.names = FALSE)
 timestamp()