scRNA-seq_analysis

pipelines/21_update_annotation/update_annotation.R

@@ -0,0 +1,434 @@
+args = commandArgs(trailingOnly=T)
+
+args = paste(args, collapse = "")
+
+args = unlist(strsplit(args, ";"))
+
+
+
+
+arguments.list = "
+
+seurat.addr.arg = args[1]
+
+make.app        = args[2]
+
+update.file        = args[3]
+
+"
+
+
+
+
+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)
+
+
+
+
+seurat.addr = file.path("../../data", seurat.addr)
+
+
+
+
+source("../../tools/bunddle_utils.R")
+
+
+
+
+library(Seurat)
+
+library(plyr)
+
+library(dplyr)
+
+library(reshape2)
+
+library(RColorBrewer)
+
+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.")
+
+
+
+# load updated annotation
+
+update.template = read.csv(update.file, stringsAsFactors = F, sep = '\t')
+
+if(dim(update.template)[2] == 1){
+
+  update.template = read.csv(update.file, stringsAsFactors = F, sep = ',')
+
+}
+
+
+
+
+# update cell labels in seurat object
+
+seurat.obj@meta.data$cell.labels = mapvalues(as.vector(seurat.obj@meta.data$LouvainClustering), from = update.template$Cluster, to = update.template$Identity)
+
+
+
+
+print("Saving seurat object")
+
+saveRDS(seurat.obj, seurat.addr)
+
+
+
+
+if (make.app){
+
+  print('Making the interactive app')
+
+  marker.genes.top = read.csv("annotation_markers.csv", stringsAsFactors = F)
+
+  # update cluster names in marker.genes.top
+
+  marker.genes.top$cluster = mapvalues(x=as.vector(marker.genes.top$cluster), from = update.template$Cluster, to = update.template$Identity)
+
+  
+
+  # now make an interactive maps
+
+  gene_sym_to_marker = marker.genes.top[, c('gene', 'cluster')]
+
+  categories = c("LouvainClustering", "fetal.ids", "sort.ids", "lanes", "stages", "gender", "doublets", "cell.labels")
+
+  genes = as.vector(unique(gene_sym_to_marker$gene))
+
+  
+
+  expression.data = as.data.frame(as.matrix(t(seurat.obj@data[genes, names(seurat.obj@ident)])))
+
+  categories.colours = rep(NA, length(categories))
+
+  categories.data = as.data.frame(seurat.obj@meta.data[names(seurat.obj@ident), categories])
+
+  for(j in 1:length(categories)){
+
+    category = categories[j]
+
+    category.colour.scheme = categories.colours[j]
+
+    if (!is.na(category.colour.scheme)){
+
+      category.colour.scheme = read.csv(category.colour.scheme)
+
+      category.colour.scheme = mapvalues(x=categories.data[, category], from=as.vector(unique(category.colour.scheme$CellTypes)), to=as.vector(unique(category.colour.scheme$Colours)))
+
+    }else{
+
+      category.colour.scheme = sample(colorRampPalette(brewer.pal(12, "Paired"))(length(as.vector(unique(categories.data[, category])))))
+
+      category.colour.scheme = mapvalues(x=categories.data[, category], from=as.vector(unique(categories.data[, category])), to=category.colour.scheme)
+
+    }
+
+    category = paste(category, "colours", sep = "_")
+
+    categories.data[, category] = category.colour.scheme
+
+  }
+
+  dim.data = seurat.obj@dr$umap@cell.embeddings[, 1:2]
+
+  expression.data = cbind(dim.data, categories.data, expression.data)
+
+  write.csv(expression.data, "./expression_data.csv", row.names = F)
+
+  
+
+  #gene.families = as.vector(unique(unlist(strsplit(as.vector(gene_sym_to_marker$cluster), "\\|"))))
+
+  gene_sym_to_marker$ClusterName = as.character(gene_sym_to_marker$cluster)
+
+  #gene_sym_to_marker$ClusterName = paste('000', gene_sym_to_marker$ClusterName, sep = '')
+
+  #gene_sym_to_marker$ClusterName = unlist(lapply(gene_sym_to_marker$ClusterName, function(cluster_name){substr(cluster_name, nchar(cluster_name) - 2, nchar(cluster_name))}))
+
+  #gene_sym_to_marker$ClusterName = paste('Cluster', gene_sym_to_marker$ClusterName, sep = '_')
+
+  
+
+  gene.families = as.vector(unique(gene_sym_to_marker$ClusterName))
+
+  
+
+  gene.to.family = c()
+
+  for(i in 1:length(gene.families)){
+
+    gene.family = gene.families[i]
+
+    gene.family = gsub(pattern="\\'", replacement="", x=gene.family)
+
+    members = as.vector(gene_sym_to_marker$gene[grep(gene_sym_to_marker$ClusterName, pattern=gene.family, value=F)])
+
+    inline = sprintf("gene_families['%s']=", gene.family)
+
+    members = paste(members, "'", sep = "")
+
+    members = paste("'", members, sep = "")
+
+    members = paste(members, collapse = ",")
+
+    members = paste("[", members, "]", sep = "")
+
+    inline = paste(inline, members)
+
+    gene.to.family = c(gene.to.family, inline)
+
+  }
+
+  all.genes = as.vector(unique(gene_sym_to_marker$gene))
+
+  all.genes = paste(all.genes, "'", sep = "")
+
+  all.genes = paste("'", all.genes, sep = "")
+
+  all.genes = paste(all.genes, collapse = ",")
+
+  all.genes = paste("gene_families['ALL']=[", all.genes, "]", sep = "")
+
+  gene.to.family = c(gene.to.family, all.genes)
+
+  gene.to.family = sort(gene.to.family)
+
+  
+
+  gene.families.file = file('gene_families.txt', "w")
+
+  writeLines(gene.to.family, gene.families.file)
+
+  close(gene.families.file)
+
+  
+
+  save.to = file.path(output_folder, 'interactive_markers.html')
+
+  n_categories = length(categories)
+
+  command = sprintf('%s html_2D_gene_expression_viewer_by_gene_family.py %s %s %s', python.addr, 
+
+                    save.to, 'expression_data.csv', n_categories)
+
+  system(command, wait = T)
+
+  
+
+  file.remove(c('./expression_data.csv', './gene_families.txt'))
+
+  
+
+  # make annotation clouds for each cluster
+
+  seurat.obj = SetAllIdent(object=seurat.obj, id='cell.labels')
+
+  
+
+  expression.data = seurat.obj@data
+
+  mito.genes = grep(pattern="^MT-", x=rownames(expression.data))
+
+  expression.data = expression.data[-c(mito.genes), ]
+
+  
+
+  gene_to_pop = read.csv("./gene_to_pop.tsv", sep = '\t', header = F)
+
+  populations = paste(as.vector(gene_to_pop$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(output_folder, 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 ... ")