306 lines
13 KiB
R
306 lines
13 KiB
R
#!/usr/bin/env Rscript
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# This script will make heatmaps for the REMc analysis
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# need to give the input "finalTable.csv" file after running REMc generated by eclipse
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library(RColorBrewer)
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library(gplots)
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args <- commandArgs(TRUE)
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# Set output dir
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if (length(args) >= 1) {
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input_finalTable <- file.path(args[1])
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} else {
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input_finalTable <- "/REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
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}
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if (length(args) >= 2) {
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outDir <- file.path(args[2])
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} else {
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outDir <- "/REMcHeatmaps/REMcWithShift.csv" # for legacy workflow
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}
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hmapfile <- data.frame(read.csv(file = input_finalTable, header = TRUE, sep = ",", stringsAsFactors = FALSE))
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# set NAs to NA
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hmapfile[hmapfile == -100] <- NA
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hmapfile[hmapfile == 100] <- NA
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hmapfile[hmapfile == 0.001] <- NA
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hmapfile[hmapfile == -0.001] <- NA
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# select the number of rows based on the number of genes
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num_total_genes <- length(hmapfile[, 1])
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# Break out the cluster names so each part of the cluster origin can be accessed
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# line below removed because it adds to many genes to clusters when going past 1-0-10
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# since it cannot differentiate between 1-0-1 and 1-0-10 when using grepl.
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# hmapfile$cluster.origin = gsub(" ","",x=hmapfile$cluster.origin)
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hmapfile$cluster.origin <- gsub(";", " ;", x = hmapfile$cluster.origin)
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hmapfile$cluster.origin <- strsplit(hmapfile$cluster.origin, ";")
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#use tail(x,n) for accessing the outward most cluster
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clust_rounds <- 0
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for (i in 1:num_total_genes) {
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if (length(hmapfile$cluster.origin[[i]]) > clust_rounds) {
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clust_rounds <- length(hmapfile$cluster.origin[[i]])
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}
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}
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unique_clusts <- unique(hmapfile$cluster.origin[1:num_total_genes])
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unique_clusts <- unique_clusts[unique_clusts != " "]
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# Select only the unique cluster names
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unique_clusts <- sort(unique(unlist(unique_clusts, use.names = FALSE)), decreasing = FALSE)
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num_unique_clusts <- length(unique_clusts)
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# Base the color key on a statistical analysis of the L and K data
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# need to create "breaks" to set the color key, need to have 12 different breaks (for 11 colors)
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# scale() will calculate the mean and standard deviation of the entire vector,
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# then "scale" each element by those values by subtracting the mean and dividing by the sd.
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# hmapfile[,4:(length(hmapfile[1,]) - 2)] <- scale(hmapfile[,4:(length(hmapfile[1,]) - 2)])
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# change so that the L data is multiplied to be on the same scale as the K data
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KEY_MIN <- 0
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KEY_MAX <- 0
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K_MIN <- 0
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L_MAX <- 0
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KcolumnValues <- vector()
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LcolumnValues <- vector()
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for (i in 4:(length(hmapfile[1, ]) - 2)){
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if (grepl("_Z_lm_K", colnames(hmapfile)[i], fixed = TRUE) == TRUE) {
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KcolumnValues <- append(KcolumnValues, i)
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}
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if (grepl("_Z_lm_L", colnames(hmapfile)[i], fixed = TRUE) == TRUE) {
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LcolumnValues <- append(LcolumnValues, i)
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}
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}
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# L_MAX <- quantile(hmapfile[,LcolumnValues],c(0,.01,.5,.99,1),na.rm=TRUE)[4]
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# K_MIN <- quantile(hmapfile[,KcolumnValues],c(0,.01,.5,.99,1),na.rm=TRUE)[2]
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# L_MAX <- quantile(hmapfile[,LcolumnValues],c(0,.01,.5,.975,1),na.rm=TRUE)[4]
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# K_MIN <- quantile(hmapfile[,KcolumnValues],c(0,.025,.5,.99,1),na.rm=TRUE)[2]
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# Z scores are
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L_MAX <- 12
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K_MIN <- -12
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# L_Multiplier <- as.numeric(abs(K_MIN/L_MAX))
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# hmapfile[,LcolumnValues] <- hmapfile[,LcolumnValues] * L_Multiplier
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# if(grepl("SHIFT",colnames(hmapfile)[4],fixed=TRUE) == TRUE){
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# print("FOUND SHIFT VALUES")
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# hmapfile[,(LcolumnValues - 1)] <- hmapfile[,(LcolumnValues-1)] * L_Multiplier
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# }
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# KEY_MAX <- as.numeric(L_MAX * L_Multiplier)
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# KEY_MIN <- as.numeric(K_MIN)
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KEY_MAX <- as.numeric(L_MAX)
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KEY_MIN <- as.numeric(K_MIN)
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print(KEY_MIN)
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print(L_MAX)
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# print(L_Multiplier)
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colormapbreaks <- c(KEY_MIN, KEY_MIN * (5 / 6), KEY_MIN * (4 / 6), KEY_MIN * (3 / 6), KEY_MIN * (2 / 6),
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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)
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# print(colormapbreaks)
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# 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?)
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# However since also using this to amend the first part.
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# Could possibly identify all the ones that contain the word shift and then create an object containing just those numbers
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# then could just use these values and create spaces only between interaction values
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# possibly could get rid of redundant shift values if we don't want to view these
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# could we pool all the shift data/average it?
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if (grepl("Shift", colnames(hmapfile)[4], fixed = TRUE) == TRUE) {
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even_columns <- seq(from = 2, to = (length(hmapfile[1, ]) - 7), by = 2)
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# ev_repeat = rep("white",length(even_columns))
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# ev_repeat = rep("red",(length(hmapfile[1,]) - 5))
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# middle_col <- (length(hmapfile[1,]) - 5)/2
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# ev_repeat[(middle_col/2)] <- "black"
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# print(ev_repeat)
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}
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if (grepl("Shift", colnames(hmapfile)[4], fixed = TRUE) == FALSE) {
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even_columns <- seq(from = 2, to = (length(hmapfile[1, ]) - 7), by = 1)
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print("NO SHIFT VALS FOUND")
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}
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# FOR THIS SCRIPT ONLY (rap tem hu script)
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# even_columns <- c(2,5,7,10,12,15,17)
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# m <- 0
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colnames_edit <- as.character(colnames(hmapfile)[4:(length(hmapfile[1, ]) - 2)])
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# print(colnames_edit)
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for (i in 1:length(colnames_edit)) {
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if (grepl("Shift", colnames_edit[i], fixed = TRUE) == TRUE) {
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colnames_edit[i] <- ""
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colnames_edit[i + 1] <- gsub(pattern = "_Z_lm_", replacement = " ", x = colnames_edit[i + 1])
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try(colnames_edit[i + 1] <- gsub(pattern = "_", replacement = " ", x = colnames_edit[i + 1]))
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# INT_store <- strsplit(colnames_edit[i+1], "Z_lm")
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# print(length(unlist(INT_store)))
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# if(length(unlist(INT_store)) == 4){
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# colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[3],sep=" ")
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# }
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# if(length(unlist(INT_store)) == 3){
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#
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# colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],sep=" ")
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# }
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# if(length(unlist(INT_store)) == 5){
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# colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[3],unlist(INT_store)[4],sep=" ")
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# }
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# if(length(unlist(INT_store)) == 6){
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# colnames_edit[i+1] <- paste(unlist(INT_store)[1],unlist(INT_store)[2],unlist(INT_store)[6],sep=" ")
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# }
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}
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}
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print(colnames_edit)
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# break()
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# colnames_edit[5] <- "TEM HLEG K"
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# colnames_edit[10] <- "TEM HL K"
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# colnames_edit[15] <- "TEM HLEG L"
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# colnames_edit[20] <- "TEM HL L"
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# Create the heatmaps
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for (i in 1:num_unique_clusts) {
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cluster <- unique_clusts[i]
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cluster_data <- subset(hmapfile, grepl(cluster, cluster.origin))
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cluster_length <- length(cluster_data[, 1])
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if (cluster_length != 1) {
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X0 <- as.matrix(cluster_data[, 4:(length(hmapfile[1, ]) - 2)])
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if (cluster_length >= 2001) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, height = 20, width = 15)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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# zlim=c(-132, 132),
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xlab = "Type of Media", ylab = "Gene Name",
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# cellnote = round(X0,digits = 0), notecex = 0.1, key = TRUE,
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keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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# ColSideColors=ev_repeat,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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# abline(v=0.5467,col="black")
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dev.off()
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}
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if (cluster_length >= 201 && cluster_length <= 2000) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, height = 15, width = 12)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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# zlim=c(-132,132),
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xlab = "Type of Media", ylab = "Gene Name",
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cellnote = round(X0, digits = 0), notecex = 0.1, key = TRUE,
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keysize = 0.7, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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# abline(v=0.5316,col="black")
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dev.off()
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}
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if (cluster_length >= 150 && cluster_length <= 200) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, height = 12, width = 12)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.1, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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# zlim=c(-132,132),
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xlab = "Type of Media", ylab = "Gene Name",
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cellnote = round(X0, digits = 0), notecex = 0.2, key = TRUE,
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keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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dev.off()
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}
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if (cluster_length >= 101 && cluster_length <= 149) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, mypath, height = 12, width = 12)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.2, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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#zlim=c(-132,132),
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xlab = "Type of Media", ylab = "Gene Name",
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cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
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keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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dev.off()
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}
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if (cluster_length >= 60 && cluster_length <= 100) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, height = 12, width = 12)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.4, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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# zlim=c(-132,132),
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xlab = "Type of Media", ylab = "Gene Name",
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cellnote = round(X0, digits = 0), notecex = 0.3, key = TRUE,
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keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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dev.off()
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}
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if (cluster_length <= 59 && cluster_length >= 30) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, height = 9, width = 12)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA, distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.6, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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# zlim=c(-132,132),
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xlab = "Type of Media", ylab = "Gene Name",
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cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
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keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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dev.off()
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}
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if (cluster_length <= 29) {
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mypath <- file.path(outDir, paste0("cluster_", gsub(" ", "", cluster), ".pdf"))
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pdf(file = mypath, height = 7, width = 12)
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heatmap.2(
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x = X0,
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Rowv = TRUE, Colv = NA,
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distfun = dist, hclustfun = hclust,
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dendrogram = "row", cexCol = 0.8, cexRow = 0.9, scale = "none",
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breaks = colormapbreaks, symbreaks = FALSE, colsep = even_columns, sepcolor = "white", offsetCol = 0.1,
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# zlim=c(-132,132),
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xlab = "Type of Media", ylab = "Gene Name",
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cellnote = round(X0, digits = 0), notecex = 0.4, key = TRUE,
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keysize = 1, trace = "none", density.info = c("none"), margins = c(10, 8),
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na.color = "red", col = brewer.pal(11, "PuOr"),
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main = cluster,
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labRow = as.character(cluster_data$Gene), labCol = colnames_edit
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)
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dev.off()
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}
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}
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# print(paste("FINISHED", "CLUSTER",cluster,sep=" "))
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}
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