scRNA-seq_analysis

pipelines/93_gene_grouping/gene_grouping.R

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+args = commandArgs(trailingOnly=T)
+args = paste(args, collapse = "")
+args = unlist(strsplit(args, ";"))
+
+arguments.list = "
+seurat.addr.arg = args[1]
+no_clusters.arg = args[2]
+"
+
+python.addr = "python"
+
+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)
+
+library(Seurat)
+library(RColorBrewer)
+library(dplyr)
+library(plyr)
+
+#######################################################################################################
+
+# load data
+print("loading data ... ")
+seurat.obj = readRDS(seurat.addr)
+print("Data loaded.")
+
+# check if LouvainClustering is present
+if ("LouvainClustering" %in% colnames(seurat.obj@meta.data)){
+  print("Identifying gene outliers but first need to aggregate gene expression by clusters")
+  seurat.obj = SetAllIdent(object=seurat.obj, id="LouvainClustering")
+  no.genes = nrow(seurat.obj@data)
+  start_index = 1
+  while (start_index < no.genes){
+    end_index = start_index + 999
+    end_index = min(end_index, no.genes)
+    expression.data_ = data.matrix(seurat.obj@data[start_index:end_index, ])
+    expression.data_ = t(expression.data_)
+    expression.data_ = as.data.frame(expression.data_)
+    expression.data_ = cbind(data.frame(CellLabels = as.vector(seurat.obj@ident)), expression.data_)
+    expression.data_ = aggregate(expression.data_[2:dim(expression.data_)[2]], list(expression.data_$CellLabels), mean)
+    expression.data_ = cbind(data.frame(CellType = expression.data_$Group.1), expression.data_[, 2:dim(expression.data_)[2]])
+    rownames(expression.data_) = expression.data_$CellType
+    expression.data_ = expression.data_[, 2:ncol(expression.data_)]
+    print(start_index)
+    if (start_index == 1){
+      expression.data = expression.data_
+    }else{
+      expression.data = cbind(expression.data, expression.data_)
+    }
+    start_index = start_index + 1000
+  }
+  # saving the expression matrix
+  write.csv(expression.data, file.path(output_folder, "expression.csv"))
+  # run python script to identify outliers
+  command = sprintf("%s clustering.py %s %s", python.addr, output_folder, no_clusters)
+  system(command, wait = T)
+  # remove the expression csv file
+  file.remove(file.path(output_folder, "expression.csv"))
+  # load gene clustering
+  gene_clustering = read.csv(file.path(output_folder, "clustering.csv"), row.names = 1)
+  # save feature plots
+  gene_names = as.vector(unique(gene_clustering$GeneNames))
+  features_folder = file.path(output_folder, "features")
+  dir.create(features_folder)
+  dr_coordinates = seurat.obj@dr$umap@cell.embeddings
+  for (i in seq_along(gene_names)){
+    gene_name = gene_names[i]
+    png_name = paste(file.path(features_folder, gene_name), "png", sep = ".")
+    dframe = data.frame(X = dr_coordinates[, 1], Y = dr_coordinates[, 2], Expression = seurat.obj@data[gene_name, ])
+    plot.obj = ggplot(dframe, aes(x = X, y = Y, color = Expression))
+    plot.obj = plot.obj + geom_point(size = .5)
+    plot.obj = plot.obj + theme_void() + theme(panel.background = element_rect(fill = 'black', colour = 'black'))
+    plot.obj = plot.obj + scale_colour_gradient(low = "blue", high = "red")
+    png(png_name, width = 500, height = 500)
+    print(plot.obj)
+    dev.off()
+    if (i %% 10 == 0){
+      print(sprintf("%s / %s", i, length(gene_names)))
+    }
+  }
+}else{
+  print("Data needs to be clustered first")
+}
+
+print("Ended beautifully ... ")