mirror of
https://github.com/haniffalab/scRNA-seq_analysis.git
synced 2024-10-23 08:29:24 -07:00
118 lines
No EOL
4.7 KiB
R
Executable file
118 lines
No EOL
4.7 KiB
R
Executable file
args = commandArgs(trailingOnly=T)
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args = paste(args, collapse = "")
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args = unlist(strsplit(args, ";"))
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arguments.list = "
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seurat.addr.arg = args[1]
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no_clusters.arg = args[2]
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"
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python.addr = "python"
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expected_arguments = unlist(strsplit(arguments.list, "\n"))
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expected_arguments = expected_arguments[!(expected_arguments == "")]
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if(length(args) != length(expected_arguments)){
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error.msg = sprintf('This pipeline requires %s parameters', as.character(length(expected_arguments)))
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expected_arguments = paste(unlist(lapply(strsplit(expected_arguments, ".arg"), "[", 1)), collapse = "\n")
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stop(sprintf('This pipeline requires %s parameters: '))
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}
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eval(parse(text = arguments.list))
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for(n in 1:length(expected_arguments)){
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argument = expected_arguments[n]
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argument = gsub(pattern=" ", replacement="", x=argument)
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argument.name = unlist(strsplit(argument, "="))[1]
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variable.name = gsub(pattern=".arg", replacement="", argument.name)
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argument.content = eval(parse(text = argument.name))
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eval(parse(text = argument.content))
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if (!exists(variable.name)){
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stop(sprintf("Argument %s not passed. Stopping ... ", variable.name))
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}
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}
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# create required folders for output and work material
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output_folder = gsub(pattern="^\\d+_", replacement="", x=basename(getwd()))
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output_folder = paste(output_folder, seurat.addr, sep = "_")
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c.time = Sys.time()
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c.time = gsub(pattern=" BST", replacement="", x=c.time)
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c.time = gsub(pattern=":", replacement="", x=c.time)
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c.time = gsub(pattern=" ", replacement="", x=c.time)
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c.time = gsub(pattern="-", replacement="", x=c.time)
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c.time = substr(x=c.time, start=3, stop=nchar(c.time))
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output_folder = paste(output_folder, c.time, sep = "_")
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output_folder = file.path("../../output", output_folder)
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dir.create(output_folder)
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library(Seurat)
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library(RColorBrewer)
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library(dplyr)
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library(plyr)
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#######################################################################################################
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# load data
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print("loading data ... ")
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seurat.obj = readRDS(seurat.addr)
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print("Data loaded.")
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# check if LouvainClustering is present
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if ("LouvainClustering" %in% colnames(seurat.obj@meta.data)){
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print("Identifying gene outliers but first need to aggregate gene expression by clusters")
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seurat.obj = SetAllIdent(object=seurat.obj, id="LouvainClustering")
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no.genes = nrow(seurat.obj@data)
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start_index = 1
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while (start_index < no.genes){
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end_index = start_index + 999
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end_index = min(end_index, no.genes)
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expression.data_ = data.matrix(seurat.obj@data[start_index:end_index, ])
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expression.data_ = t(expression.data_)
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expression.data_ = as.data.frame(expression.data_)
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expression.data_ = cbind(data.frame(CellLabels = as.vector(seurat.obj@ident)), expression.data_)
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expression.data_ = aggregate(expression.data_[2:dim(expression.data_)[2]], list(expression.data_$CellLabels), mean)
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expression.data_ = cbind(data.frame(CellType = expression.data_$Group.1), expression.data_[, 2:dim(expression.data_)[2]])
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rownames(expression.data_) = expression.data_$CellType
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expression.data_ = expression.data_[, 2:ncol(expression.data_)]
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print(start_index)
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if (start_index == 1){
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expression.data = expression.data_
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}else{
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expression.data = cbind(expression.data, expression.data_)
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}
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start_index = start_index + 1000
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}
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# saving the expression matrix
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write.csv(expression.data, file.path(output_folder, "expression.csv"))
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# run python script to identify outliers
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command = sprintf("%s clustering.py %s %s", python.addr, output_folder, no_clusters)
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system(command, wait = T)
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# remove the expression csv file
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file.remove(file.path(output_folder, "expression.csv"))
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# load gene clustering
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gene_clustering = read.csv(file.path(output_folder, "clustering.csv"), row.names = 1)
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# save feature plots
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gene_names = as.vector(unique(gene_clustering$GeneNames))
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features_folder = file.path(output_folder, "features")
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dir.create(features_folder)
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dr_coordinates = seurat.obj@dr$umap@cell.embeddings
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for (i in seq_along(gene_names)){
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gene_name = gene_names[i]
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png_name = paste(file.path(features_folder, gene_name), "png", sep = ".")
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dframe = data.frame(X = dr_coordinates[, 1], Y = dr_coordinates[, 2], Expression = seurat.obj@data[gene_name, ])
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plot.obj = ggplot(dframe, aes(x = X, y = Y, color = Expression))
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plot.obj = plot.obj + geom_point(size = .5)
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plot.obj = plot.obj + theme_void() + theme(panel.background = element_rect(fill = 'black', colour = 'black'))
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plot.obj = plot.obj + scale_colour_gradient(low = "blue", high = "red")
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png(png_name, width = 500, height = 500)
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print(plot.obj)
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dev.off()
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if (i %% 10 == 0){
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print(sprintf("%s / %s", i, length(gene_names)))
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}
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}
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}else{
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print("Data needs to be clustered first")
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}
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print("Ended beautifully ... ") |