diff --git a/.gitignore b/.gitignore
index e900ca98..0ea15735 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@ manual.odt
 mwe
 centos-upgrade-plan.txt
 workflow/out/
+workflow/scans/
diff --git a/workflow/apps/r/addShiftVals.R b/workflow/apps/r/addShiftVals.R
index 08a46d3f..7ae5988a 100644
--- a/workflow/apps/r/addShiftVals.R
+++ b/workflow/apps/r/addShiftVals.R
@@ -10,25 +10,25 @@ library(sos)
 args <- commandArgs(TRUE)
 
 if (length(args) >= 1) {
-  finalTable <- args[1]
+  finalTable <- file.path(args[1])
 } else {
   finalTable <- "REMcRdy_lm_only.csv-finalTable.csv"  # for legacy workflow
 }
 
 if (length(args) >= 2) {
-  shiftFile <- args[2]
+  shiftFile <- file.path(args[2])
 } else {
   shiftFile <- "Shift_only.csv"  # for legacy workflow
 }
 
 if (length(args) >= 3) {
-  studyInfo <- args[3]
+  studyInfo <- file.path(args[3])
 } else {
   studyInfo <- "../Code/StudyInfo.csv" # for legacy workflow
 }
 
 if (length(args) >= 4) {
-  output <- args[4]
+  output <- file.path(args[4])
 } else {
   output <- "REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
 }
diff --git a/workflow/apps/r/createHeatMaps.R b/workflow/apps/r/createHeatMaps.R
index 32c46cc8..dcb3d4c2 100644
--- a/workflow/apps/r/createHeatMaps.R
+++ b/workflow/apps/r/createHeatMaps.R
@@ -171,7 +171,7 @@ for (i in 1:num_unique_clusts) {
   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")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, height = 20, width = 15)
       heatmap.2(
         x = X0,
@@ -191,7 +191,7 @@ for (i in 1:num_unique_clusts) {
       dev.off()
     }
     if (cluster_length >= 201 && cluster_length <= 2000) {
-      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, height = 15, width = 12)
       heatmap.2(
         x = X0,
@@ -210,7 +210,7 @@ for (i in 1:num_unique_clusts) {
       dev.off()
     }
     if (cluster_length >= 150 && cluster_length <= 200) {
-      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, height = 12, width = 12)
       heatmap.2(
         x = X0,
@@ -228,7 +228,7 @@ for (i in 1:num_unique_clusts) {
       dev.off()
     }
     if (cluster_length >= 101 && cluster_length <= 149) {
-      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, mypath, height = 12, width = 12)
       heatmap.2(
         x = X0,
@@ -246,7 +246,7 @@ for (i in 1:num_unique_clusts) {
       dev.off()
     }
     if (cluster_length >= 60 && cluster_length <= 100) {
-      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, height = 12, width = 12)
       heatmap.2(
         x = X0,
@@ -264,7 +264,7 @@ for (i in 1:num_unique_clusts) {
       dev.off()
     }
     if (cluster_length <= 59 && cluster_length >= 30) {
-      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, height = 9, width = 12)
       heatmap.2(
         x = X0,
@@ -282,7 +282,7 @@ for (i in 1:num_unique_clusts) {
       dev.off()
     }
     if (cluster_length <= 29) {
-      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), sep = ""), ".pdf")
+      mypath <- file.path(outDir, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
       pdf(file = mypath, height = 7, width = 12)
       heatmap.2(
         x = X0,
diff --git a/workflow/apps/r/createHeatMapsHomology.R b/workflow/apps/r/createHeatMapsHomology.R
index b376a22a..029f0b7e 100644
--- a/workflow/apps/r/createHeatMapsHomology.R
+++ b/workflow/apps/r/createHeatMapsHomology.R
@@ -8,57 +8,58 @@ library(tidyverse)
 
 args <- commandArgs(TRUE)
 # Need to give the input "finalTable.csv" file after running REMc generated by eclipse
-inputFinalTable <- args[1]
+inputFinalTable <- file.path(args[1])
 
 # Give the DAmP_list.txt as the third argument - will color the gene names differently
-DAmPs <- Args[2]
-DAmP_list <- read.delim(file=DAmPs,header=F,stringsAsFactors = F)
+DAmPs <- file.path(Args[2])
+DAmP_list <- read.delim(file = DAmPs, header = FALSE, stringsAsFactors = FALSE)
 
 # Give the yeast human homology mapping as the fourth argument - will add the genes to the finalTable and use info for heatmaps
-mapFile <- Args[3]
-mapping <- read.csv(file=mapFile,stringsAsFactors = F)
+mapFile <- file.path(Args[3])
+mapping <- read.csv(file = mapFile, stringsAsFactors = FALSE)
 
 # Define the output path for the heatmaps - create this folder first - in linux terminal in the working folder use > mkdir filename_heatmaps
-outputPath <- Args[4]
+outputPath <- file.path(Args[4])
 
 # Read in finalTablewithShift
-hmapfile <- data.frame(read.csv(file=inputFinalTable,header=TRUE,sep=",",stringsAsFactors = FALSE))
+hmapfile <- data.frame(read.csv(file = inputFinalTable, header = TRUE, sep = ",", stringsAsFactors = FALSE))
 
 # Map the finalTable to the human homolog file
 hmapfile_map <- hmapfile
-# Match using OrfRep after dropping the _1 _2 _3 _4 
+# Match using OrfRep after dropping the _1 _2 _3 _4
 # But need to also account for some older files have ORF as column name rather than OrfRep in finalTable file
-if(colnames(hmapfile_map)[2] == "OrfRep"){
+if (colnames(hmapfile_map)[2] == "OrfRep") {
   try(hmapfile_map$ORFMatch <- hmapfile_map$OrfRep)
 }
-if(colnames(hmapfile_map)[2] == "ORF"){
+if (colnames(hmapfile_map)[2] == "ORF") {
   try(hmapfile_map$ORFMatch <- hmapfile_map$ORF)
 }
-hmapfile_map$ORFMatch <- gsub("_1","",x=hmapfile_map$ORFMatch)
-hmapfile_map$ORFMatch <- gsub("_2","",x=hmapfile_map$ORFMatch)
-hmapfile_map$ORFMatch <- gsub("_3","",x=hmapfile_map$ORFMatch)
-hmapfile_map$ORFMatch <- gsub("_4","",x=hmapfile_map$ORFMatch)
+hmapfile_map$ORFMatch <- gsub("_1", "", x = hmapfile_map$ORFMatch)
+hmapfile_map$ORFMatch <- gsub("_2", "", x = hmapfile_map$ORFMatch)
+hmapfile_map$ORFMatch <- gsub("_3", "", x = hmapfile_map$ORFMatch)
+hmapfile_map$ORFMatch <- gsub("_4", "", x = hmapfile_map$ORFMatch)
 
-# Join the hmapfile using 
-hmapfile_w_homolog <- full_join(hmapfile_map,mapping,by=c("ORFMatch"="ensembl_gene_id"))
+# Join the hmapfile using
+hmapfile_w_homolog <- full_join(hmapfile_map, mapping, by = c("ORFMatch" = "ensembl_gene_id"))
 
 # Remove matches that are not from the finalTable
-hmapfile_w_homolog <- hmapfile_w_homolog[is.na(hmapfile_w_homolog$likelihood) == F,]
+hmapfile_w_homolog <- hmapfile_w_homolog[is.na(hmapfile_w_homolog$likelihood) == FASLE, ]
 
 # Write csv with all info from mapping file
-write.csv(hmapfile_w_homolog,file=paste(outputPath,"/",inputFinalTable,"_WithHomologAll.csv",sep=""),row.names = F)
+write.csv(hmapfile_w_homolog, file.path(outputPath, paste(inputFinalTable, "_WithHomologAll.csv", sep = "")), row.names = FALSE)
 
 # Remove the non matches and output another mapping file - this is also one used to make heatmaps
-hmapfile_w_homolog <- hmapfile_w_homolog[is.na(hmapfile_w_homolog$external_gene_name_Human) == F,]
-write.csv(hmapfile_w_homolog,file=paste(outputPath,"/",inputFinalTable,"_WithHomologMatchesOnly.csv",sep=""),row.names = F)
+hmapfile_w_homolog <- hmapfile_w_homolog[is.na(hmapfile_w_homolog$external_gene_name_Human) == FALSE, ]
+write.csv(hmapfile_w_homolog, file.path(outputPath, paste(inputFinalTable, "_WithHomologMatchesOnly.csv", sep = ""), row.names = FALSE))
 
 # Add human gene name to the Gene column
-hmapfile_w_homolog$Gene <- paste(hmapfile_w_homolog$Gene,hmapfile_w_homolog$external_gene_name_Human,sep="/")
+hmapfile_w_homolog$Gene <- paste(hmapfile_w_homolog$Gene, hmapfile_w_homolog$external_gene_name_Human, sep = "/")
 
 # Only keep the finalTable file columns and the homology info
 hmap_len <- dim(hmapfile)[2]
 
-hmapfile_w_homolog_remake <- cbind(hmapfile_w_homolog[,1:hmap_len], hsapiens_homolog_orthology_type=hmapfile_w_homolog$hsapiens_homolog_orthology_type)
+hmapfile_w_homolog_remake <-
+  cbind(hmapfile_w_homolog[, 1:hmap_len], hsapiens_homolog_orthology_type = hmapfile_w_homolog$hsapiens_homolog_orthology_type)
 hmapfile <- hmapfile_w_homolog_remake
 
 # Set NAs to NA
@@ -68,19 +69,20 @@ 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.
-# hmapfile$cluster.origin = gsub(" ","",x=hmapfile$cluster.origin)
+# 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.
+# 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$cluster.origin[[i]])
   }
 }
@@ -89,12 +91,13 @@ unique_clusts <- unique(hmapfile$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)])
 
@@ -107,19 +110,19 @@ L_MAX <- 0
 KcolumnValues <- vector()
 LcolumnValues <- vector()
 
-for(i in 4:(length(hmapfile[1,]) - 3)){
-  if(grepl("_Z_lm_K",colnames(hmapfile)[i],fixed=TRUE) == TRUE){
-    KcolumnValues <- append(KcolumnValues,i)
+for (i in 4:(length(hmapfile[1, ]) - 3)){
+  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)
   }
 }
 
-# L_MAX <- quantile(hmapfile[,LcolumnValues],c(0,.01,.5,.99,1),na.rm=TRUE)[4]
-# K_MIN <- quantile(hmapfile[,KcolumnValues],c(0,.01,.5,.99,1),na.rm=TRUE)[2]
-# L_MAX <- quantile(hmapfile[,LcolumnValues],c(0,.01,.5,.975,1),na.rm=TRUE)[4]
-# K_MIN <- quantile(hmapfile[,KcolumnValues],c(0,.025,.5,.99,1),na.rm=TRUE)[2]
+# L_MAX <- quantile(hmapfile[,LcolumnValues],c(0,.01,.5,.99,1),na.rm = TRUE)[4]
+# K_MIN <- quantile(hmapfile[,KcolumnValues],c(0,.01,.5,.99,1),na.rm = TRUE)[2]
+# L_MAX <- quantile(hmapfile[,LcolumnValues],c(0,.01,.5,.975,1),na.rm = TRUE)[4]
+# K_MIN <- quantile(hmapfile[,KcolumnValues],c(0,.025,.5,.99,1),na.rm = TRUE)[2]
 
 # Z scores are
 L_MAX <- 12
@@ -128,7 +131,7 @@ K_MIN <- -12
 # L_Multiplier <- as.numeric(abs(K_MIN/L_MAX))
 # hmapfile[,LcolumnValues] <- hmapfile[,LcolumnValues] * L_Multiplier
 
-# if(grepl("SHIFT",colnames(hmapfile)[4],fixed=TRUE) == TRUE){
+# if(grepl("SHIFT",colnames(hmapfile)[4],fixed = TRUE) == TRUE){
 #  print("FOUND SHIFT VALUES")
 #  hmapfile[,(LcolumnValues - 1)] <- hmapfile[,(LcolumnValues-1)] * L_Multiplier
 # }
@@ -142,15 +145,19 @@ 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)
-#print(colormapbreaks)
+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)
+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
@@ -158,8 +165,8 @@ if(grepl("Shift",colnames(hmapfile)[4],fixed=TRUE) == TRUE){
   # 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")
 }
 
@@ -167,43 +174,43 @@ if(grepl("Shift",colnames(hmapfile)[4],fixed=TRUE) == FALSE){
 # even_columns <- c(2,5,7,10,12,15,17)
 
 # m <- 0
-colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1,]) - 3)])
+colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1, ]) - 3)])
 
 colnames(DAmP_list)[1] <- "ORF"
 hmapfile$DAmPs <- "YKO"
 colnames(hmapfile)[2] <- "ORF"
-try(hmapfile[hmapfile$ORF %in% DAmP_list$ORF,]$DAmPs <- "YKD")
+try(hmapfile[hmapfile$ORF %in% DAmP_list$ORF, ]$DAmPs <- "YKD")
 # X <- X[order(X$DAmPs,decreasing = TRUE),]
 hmapfile$color2 <- NA
-try(hmapfile[hmapfile$DAmPs == "YKO",]$color2 <- "black")
-try(hmapfile[hmapfile$DAmPs == "YKD",]$color2 <- "red")
+try(hmapfile[hmapfile$DAmPs == "YKO", ]$color2 <- "black")
+try(hmapfile[hmapfile$DAmPs == "YKD", ]$color2 <- "red")
 
 hmapfile$color <- NA
-try(hmapfile[hmapfile$hsapiens_homolog_orthology_type == "ortholog_many2many",]$color <- "#F8766D")
-try(hmapfile[hmapfile$hsapiens_homolog_orthology_type == "ortholog_one2many",]$color <- "#00BA38")
-try(hmapfile[hmapfile$hsapiens_homolog_orthology_type == "ortholog_one2one",]$color <- "#619CFF")
+try(hmapfile[hmapfile$hsapiens_homolog_orthology_type == "ortholog_many2many", ]$color <- "#F8766D")
+try(hmapfile[hmapfile$hsapiens_homolog_orthology_type == "ortholog_one2many", ]$color <- "#00BA38")
+try(hmapfile[hmapfile$hsapiens_homolog_orthology_type == "ortholog_one2one", ]$color <- "#619CFF")
 
 # print(colnames_edit)
-for(i in 1:length(colnames_edit)){
-  if(grepl("Shift",colnames_edit[i],fixed=TRUE) == TRUE){
+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)))
     # if(length(unlist(INT_store)) == 4){
-    #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[3],sep=" ")
+    #   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=" ")
+    #
+    #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],sep = " ")
     # }
     # if(length(unlist(INT_store)) == 5){
-    #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[3],unlist(INT_store)[4],sep=" ")
+    #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[3],unlist(INT_store)[4],sep = " ")
     # }
     # if(length(unlist(INT_store)) == 6){
-    #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[6],sep=" ")
+    #   colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[6],sep = " ")
     # }
 
   }
@@ -217,135 +224,142 @@ print(colnames_edit)
 # 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,]) - 6)])
-    if(cluster_length >= 2001){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=20,width=15)
+  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, ]) - 6)])
+    if (cluster_length >= 2001) {
+      mypath <- file.path(outputPath, 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,
+        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), 
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
-      # abline(v=0.5467,col="black")
+        # cellnote = round(X0,digits = 0), notecex = 0.1, key = TRUE,
+        keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        # ColSideColors = ev_repeat,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
+      # abline(v = 0.5467,col = "black")
       dev.off()
     }
-    if(cluster_length >= 201 && cluster_length <= 2000){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=15,width=12)
+    if (cluster_length >= 201 && cluster_length <= 2000) {
+      mypath <- file.path(outputPath, 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,
+        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), 
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
-      # abline(v=0.5316,col="black")
+        cellnote = round(X0, digits = 0), notecex = 0.1, key = TRUE,
+        keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
+      # abline(v = 0.5316,col = "black")
       dev.off()
     }
-    if(cluster_length >= 150 && cluster_length <= 200){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=12,width=12)
+    if (cluster_length >= 150 && cluster_length <= 200) {
+      mypath <- file.path(outputPath, 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,
+        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), 
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
+        cellnote = round(X0, digits = 0), notecex = 0.2, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
       dev.off()
     }
-    if(cluster_length >= 101 && cluster_length <= 149){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,mypath,height=12,width=12)
+    if (cluster_length >= 101 && cluster_length <= 149) {
+      mypath <- file.path(outputPath, 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,
+        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), 
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
+        cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
       dev.off()
     }
-    if(cluster_length >= 60 && cluster_length <= 100){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=12,width=12)
+    if (cluster_length >= 60 && cluster_length <= 100) {
+      mypath <- file.path(outputPath, 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,
+        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),
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
+        cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
       dev.off()
     }
-    if(cluster_length <= 59 && cluster_length >= 30){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=9,width=12)
+    if (cluster_length <= 59 && cluster_length >= 30) {
+      mypath <- file.path(outputPath, 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,
+        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), 
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
+        cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
       dev.off()
     }
-    if(cluster_length <= 29){
-      mypath = file.path(outputPath,paste("cluster_",gsub(" ","",cluster), ".pdf",sep=""))
-      pdf(file=mypath,height=7,width=12)
+    if (cluster_length <= 29) {
+      mypath <- file.path(outputPath, paste("cluster_", gsub(" ", "", cluster), ".pdf", sep = ""))
+      pdf(file = mypath, height = 7, width = 12)
       heatmap.2(
-        x=X0,
-        Rowv=TRUE, Colv=NA, 
+        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), 
+        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$Gene), labCol=colnames_edit, colRow=cluster_data$color2,RowSideColors=cluster_data$color)
+        cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
+        keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
+        na.color = "red", col = brewer.pal(11, "PuOr"),
+        main = cluster,
+        labRow = as.character(cluster_data$Gene), labCol = colnames_edit, colRow = cluster_data$color2, RowSideColors = cluster_data$color
+      )
       dev.off()
     }
   }
-  # print(paste("FINISHED", "CLUSTER",cluster,sep=" "))
+  # print(paste("FINISHED", "CLUSTER",cluster,sep = " "))
 }