scRNA-seq_analysis

pipelines/20_wordclouds/wordclouds.R

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+args = commandArgs(trailingOnly=T)
+args = paste(args, collapse = "")
+args = unlist(strsplit(args, ";"))
+
+arguments.list = "
+seurat.addr.arg = args[1]
+set.ident.arg   = args[2]
+"
+
+expected_arguments = unlist(strsplit(arguments.list, "\n"))
+expected_arguments = expected_arguments[!(expected_arguments == "")]
+
+if(length(args) != length(expected_arguments)){
+  error.msg = sprintf('This pipeline requires %s parameters', as.character(length(expected_arguments)))
+  expected_arguments = paste(unlist(lapply(strsplit(expected_arguments, ".arg"), "[", 1)), collapse = "\n")
+  stop(sprintf('This pipeline requires %s parameters: '))
+}
+
+eval(parse(text = arguments.list))
+
+for(n in 1:length(expected_arguments)){
+  argument = expected_arguments[n]
+  argument = gsub(pattern=" ", replacement="", x=argument)
+  argument.name = unlist(strsplit(argument, "="))[1]
+  variable.name = gsub(pattern=".arg", replacement="", argument.name)
+  argument.content = eval(parse(text = argument.name))
+  eval(parse(text = argument.content))
+  if (!exists(variable.name)){
+    stop(sprintf("Argument %s not passed. Stopping ... ", variable.name))
+  }
+}
+
+# create required folders for output and work material
+output_folder = gsub(pattern="^\\d+_", replacement="", x=basename(getwd()))
+output_folder = paste(output_folder, seurat.addr, sep = "_")
+c.time = Sys.time()
+c.time = gsub(pattern=" BST", replacement="", x=c.time)
+c.time = gsub(pattern=":", replacement="", x=c.time)
+c.time = gsub(pattern=" ", replacement="", x=c.time)
+c.time = gsub(pattern="-", replacement="", x=c.time)
+c.time = substr(x=c.time, start=3, stop=nchar(c.time))
+output_folder = paste(output_folder, c.time, sep = "_")
+output_folder = file.path("../../output", output_folder)
+dir.create(output_folder)
+dir.create(file.path(output_folder, "gene_word_clouds"))
+dir.create(file.path(output_folder, "celltype_word_clouds"))
+
+seurat.addr = file.path("../../data", seurat.addr)
+
+source("../../tools/bunddle_utils.R")
+
+library(Seurat)
+library(RColorBrewer)
+library(plyr)
+library(dplyr)
+library(wordcloud)
+
+
+gene_to_weighted_cell_mention = function(gene.expr){
+  idx = which(as.vector(gene_to_pop$V1) %in% names(gene.expr))
+  gene.expr = gene.expr[as.vector(gene_to_pop$V1)[idx]]
+  pop.expr = c()
+  pop.names = c()
+  for (k in 1:length(idx)){
+    gene.name = names(gene.expr)[k]
+    gene.value = gene.expr[k]
+    pop.flags = as.vector(gene_to_pop$V2)[as.vector(gene_to_pop$V1) == gene.name]
+    pop.flags = unlist(strsplit(pop.flags, ", "))
+    for (p in 1:length(pop.flags)){
+      pop.flag = pop.flags[p]
+      gene.v = 100 * gene.value / populations.weight[pop.flag]
+      if (pop.flag %in% pop.names){
+        pop.expr[pop.flag] = pop.expr[pop.flag] + gene.v
+      }else{
+        pop.names = c(pop.names, pop.flag)
+        pop.expr = c(pop.expr, gene.v)
+        names(pop.expr) = pop.names
+      }
+    }
+  }
+  pop.expr
+}
+
+#######################################################################################################
+
+# load data
+print("loading data ... ")
+seurat.obj = readRDS(seurat.addr)
+print("Data loaded.")
+
+seurat.obj = SetAllIdent(object=seurat.obj, id=set.ident)
+
+print('Making gene clouds')
+idents = as.vector(unique(seurat.obj@ident))
+for (i  in 1:length(idents)){
+  ident = idents[i]
+  ident = names(seurat.obj@ident)[seurat.obj@ident == ident]
+  expression.data = as.matrix(seurat.obj@data[,ident])
+  expression.data = rowMeans(expression.data)
+  genes = names(expression.data)
+  genes = genes[!(genes %in% genes[grep(pattern='^MT-', x=genes)])]
+  expression.data = expression.data[genes]
+  fname = sprintf('%s/%s.pdf', file.path(output_folder, "gene_word_clouds"), gsub(pattern="/", replacement="-", x=idents[i]))
+  pdf(fname, width = 15, height = 15)
+  freq.weight = round(expression.data * 100)
+  wordcloud(words=names(expression.data), freq.weight, min.freq = 1, max.words=500, 
+            random.order=FALSE, rot.per=0.0, colors=brewer.pal(8, "Dark2"),
+            order.color = T)
+  dev.off()
+}
+
+print('Making cell type flags clouds')
+
+
+expression.data = seurat.obj@data
+mito.genes = grep(pattern="^MT-", x=rownames(expression.data))
+expression.data = expression.data[-c(mito.genes), ]
+
+gene_to_pop = read.csv("./gene_to_pop.tsv", sep = '\t', header = F)
+populations = paste(as.vector(gene_to_pop$V2), collapse = ", ")
+populations = unlist(strsplit(populations, ", "))
+populations.table = table(populations)
+populations.weight = as.vector(populations.table)
+names(populations.weight) = names(populations.table)
+
+idents = as.vector(unique(seurat.obj@ident))
+for (i  in 1:length(idents)){
+  ident = idents[i]
+  print(ident)
+  ident = names(seurat.obj@ident)[seurat.obj@ident == ident]
+  expression.data = as.matrix(seurat.obj@data[,ident])
+  expression.data = rowMeans(expression.data)
+  genes = names(expression.data)
+  genes = genes[!(genes %in% genes[grep(pattern='^MT-', x=genes)])]
+  expression.data = expression.data[genes]
+  pop.expr = gene_to_weighted_cell_mention(expression.data)
+  clouder = round(100 * pop.expr)
+  fname = sprintf('%s.pdf', idents[i])
+  fname = gsub(pattern="/", replacement="-", x=fname)
+  fname = file.path(file.path(output_folder, "celltype_word_clouds"), fname)
+  pdf(fname, width = 10, height = 10)
+  wordcloud(words=names(clouder), clouder, min.freq = 1, max.words=500, 
+            random.order=FALSE, rot.per=0.0, colors=brewer.pal(8, "Dark2"),
+            order.color = T)
+  dev.off()
+}
+
+print("Ended beautifully ... ")