mirror of
https://github.com/haniffalab/scRNA-seq_analysis.git
synced 2024-10-23 08:29:24 -07:00
434 lines
8.9 KiB
R
Executable file
434 lines
8.9 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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make.app = args[2]
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update.file = args[3]
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"
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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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seurat.addr = file.path("../../data", seurat.addr)
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source("../../tools/bunddle_utils.R")
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library(Seurat)
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library(plyr)
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library(dplyr)
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library(reshape2)
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library(RColorBrewer)
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library(wordcloud)
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gene_to_weighted_cell_mention = function(gene.expr){
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idx = which(as.vector(gene_to_pop$V1) %in% names(gene.expr))
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gene.expr = gene.expr[as.vector(gene_to_pop$V1)[idx]]
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pop.expr = c()
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pop.names = c()
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for (k in 1:length(idx)){
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gene.name = names(gene.expr)[k]
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gene.value = gene.expr[k]
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pop.flags = as.vector(gene_to_pop$V2)[as.vector(gene_to_pop$V1) == gene.name]
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pop.flags = unlist(strsplit(pop.flags, ", "))
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for (p in 1:length(pop.flags)){
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pop.flag = pop.flags[p]
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gene.v = 100 * gene.value / populations.weight[pop.flag]
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if (pop.flag %in% pop.names){
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pop.expr[pop.flag] = pop.expr[pop.flag] + gene.v
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}else{
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pop.names = c(pop.names, pop.flag)
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pop.expr = c(pop.expr, gene.v)
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names(pop.expr) = pop.names
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}
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}
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}
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pop.expr
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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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# load updated annotation
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update.template = read.csv(update.file, stringsAsFactors = F, sep = '\t')
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if(dim(update.template)[2] == 1){
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update.template = read.csv(update.file, stringsAsFactors = F, sep = ',')
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}
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# update cell labels in seurat object
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seurat.obj@meta.data$cell.labels = mapvalues(as.vector(seurat.obj@meta.data$LouvainClustering), from = update.template$Cluster, to = update.template$Identity)
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print("Saving seurat object")
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saveRDS(seurat.obj, seurat.addr)
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if (make.app){
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print('Making the interactive app')
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marker.genes.top = read.csv("annotation_markers.csv", stringsAsFactors = F)
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# update cluster names in marker.genes.top
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marker.genes.top$cluster = mapvalues(x=as.vector(marker.genes.top$cluster), from = update.template$Cluster, to = update.template$Identity)
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# now make an interactive maps
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gene_sym_to_marker = marker.genes.top[, c('gene', 'cluster')]
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categories = c("LouvainClustering", "fetal.ids", "sort.ids", "lanes", "stages", "gender", "doublets", "cell.labels")
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genes = as.vector(unique(gene_sym_to_marker$gene))
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expression.data = as.data.frame(as.matrix(t(seurat.obj@data[genes, names(seurat.obj@ident)])))
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categories.colours = rep(NA, length(categories))
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categories.data = as.data.frame(seurat.obj@meta.data[names(seurat.obj@ident), categories])
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for(j in 1:length(categories)){
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category = categories[j]
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category.colour.scheme = categories.colours[j]
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if (!is.na(category.colour.scheme)){
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category.colour.scheme = read.csv(category.colour.scheme)
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category.colour.scheme = mapvalues(x=categories.data[, category], from=as.vector(unique(category.colour.scheme$CellTypes)), to=as.vector(unique(category.colour.scheme$Colours)))
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}else{
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category.colour.scheme = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(as.vector(unique(categories.data[, category])))))
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category.colour.scheme = mapvalues(x=categories.data[, category], from=as.vector(unique(categories.data[, category])), to=category.colour.scheme)
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}
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category = paste(category, "colours", sep = "_")
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categories.data[, category] = category.colour.scheme
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}
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dim.data = seurat.obj@dr$umap@cell.embeddings[, 1:2]
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expression.data = cbind(dim.data, categories.data, expression.data)
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write.csv(expression.data, "./expression_data.csv", row.names = F)
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#gene.families = as.vector(unique(unlist(strsplit(as.vector(gene_sym_to_marker$cluster), "\\|"))))
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gene_sym_to_marker$ClusterName = as.character(gene_sym_to_marker$cluster)
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#gene_sym_to_marker$ClusterName = paste('000', gene_sym_to_marker$ClusterName, sep = '')
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#gene_sym_to_marker$ClusterName = unlist(lapply(gene_sym_to_marker$ClusterName, function(cluster_name){substr(cluster_name, nchar(cluster_name) - 2, nchar(cluster_name))}))
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#gene_sym_to_marker$ClusterName = paste('Cluster', gene_sym_to_marker$ClusterName, sep = '_')
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gene.families = as.vector(unique(gene_sym_to_marker$ClusterName))
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gene.to.family = c()
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for(i in 1:length(gene.families)){
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gene.family = gene.families[i]
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gene.family = gsub(pattern="\\'", replacement="", x=gene.family)
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members = as.vector(gene_sym_to_marker$gene[grep(gene_sym_to_marker$ClusterName, pattern=gene.family, value=F)])
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inline = sprintf("gene_families['%s']=", gene.family)
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members = paste(members, "'", sep = "")
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members = paste("'", members, sep = "")
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members = paste(members, collapse = ",")
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members = paste("[", members, "]", sep = "")
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inline = paste(inline, members)
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gene.to.family = c(gene.to.family, inline)
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}
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all.genes = as.vector(unique(gene_sym_to_marker$gene))
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all.genes = paste(all.genes, "'", sep = "")
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all.genes = paste("'", all.genes, sep = "")
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all.genes = paste(all.genes, collapse = ",")
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all.genes = paste("gene_families['ALL']=[", all.genes, "]", sep = "")
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gene.to.family = c(gene.to.family, all.genes)
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gene.to.family = sort(gene.to.family)
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gene.families.file = file('gene_families.txt', "w")
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writeLines(gene.to.family, gene.families.file)
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close(gene.families.file)
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save.to = file.path(output_folder, 'interactive_markers.html')
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n_categories = length(categories)
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command = sprintf('%s html_2D_gene_expression_viewer_by_gene_family.py %s %s %s', python.addr,
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save.to, 'expression_data.csv', n_categories)
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system(command, wait = T)
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file.remove(c('./expression_data.csv', './gene_families.txt'))
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# make annotation clouds for each cluster
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seurat.obj = SetAllIdent(object=seurat.obj, id='cell.labels')
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expression.data = seurat.obj@data
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mito.genes = grep(pattern="^MT-", x=rownames(expression.data))
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expression.data = expression.data[-c(mito.genes), ]
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gene_to_pop = read.csv("./gene_to_pop.tsv", sep = '\t', header = F)
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populations = paste(as.vector(gene_to_pop$V2), collapse = ", ")
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populations = unlist(strsplit(populations, ", "))
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populations.table = table(populations)
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populations.weight = as.vector(populations.table)
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names(populations.weight) = names(populations.table)
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idents = as.vector(unique(seurat.obj@ident))
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for (i in 1:length(idents)){
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ident = idents[i]
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print(ident)
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ident = names(seurat.obj@ident)[seurat.obj@ident == ident]
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expression.data = as.matrix(seurat.obj@data[,ident])
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expression.data = rowMeans(expression.data)
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genes = names(expression.data)
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genes = genes[!(genes %in% genes[grep(pattern='^MT-', x=genes)])]
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expression.data = expression.data[genes]
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pop.expr = gene_to_weighted_cell_mention(expression.data)
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clouder = round(100 * pop.expr)
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fname = sprintf('%s.pdf', idents[i])
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fname = gsub(pattern="/", replacement="-", x=fname)
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fname = file.path(output_folder, fname)
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pdf(fname, width = 10, height = 10)
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wordcloud(words=names(clouder), clouder, min.freq = 1, max.words=500,
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random.order=FALSE, rot.per=0.0, colors=brewer.pal(8, "Dark2"),
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order.color = T)
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dev.off()
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}
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}
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print("Ended beautifully ... ")
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