scRNA-seq_analysis

pipelines/01_seurat_from_count_tables/seurat_from_count_tables.R

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+args = commandArgs(trailingOnly=T)
+args = paste(args, collapse = "")
+args = unlist(strsplit(args, ";"))
+if(length(args) != 8){
+  stop('This pipeline requires 8 parameters: organ, ProjectName, save.at (name of RDS file where processed data are saved), sequencing.types (normal, 5GEX or VDJ), annotate.cells (boolean), identify.doublets (boolean), cell.type.SVM (folder name where cell type svm classifier for given organ is stored), doublet.svm (folder name where singlet/doublet svm classifier for given organ is stored);')
+}
+
+arguments.list = "
+organ.arg             = args[1]
+ProjectName.arg       = args[2]
+save.at.arg           = args[3]
+sequencing.types.arg  = args[4]
+annotate.cells.arg    = args[5]
+identify.doublets.arg = args[6]
+cell.type.SVM.arg     = args[7]
+doublet.svm.arg       = args[8]
+"
+
+eval(parse(text = arguments.list))
+
+arguments.list = unlist(strsplit(arguments.list, "\n"))
+arguments.list = arguments.list[!(arguments.list == "")]
+
+for(n in 1:length(arguments.list)){
+  argument = arguments.list[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, save.at, 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)
+
+source("../../tools/bunddle_utils.R")
+
+sequencing.types = unlist(strsplit(sequencing.types, "-"))
+save.at = file.path("../../data", save.at)
+key.fname = "../../resources/key.csv"
+sample.key.fname = "../../resources/sample_key.csv"
+
+library(Seurat)
+library(plyr)
+library(dplyr)
+
+# function to load sequentiall 10x data from many folders passed as a vector
+# return a seurat object created from merging all the 10X data in the folders
+# does not apply any filtering
+load_10x_data_from_folders = function(folders, inside = "filtered/GRCh38/", key, sample.key){
+  # load data from first folder. if only one folder then return
+  sample.col.label = folders[1]
+  nfolders = length(folders)
+  prelabel.a = key$Prelabel[key$SUPPLIER.SAMPLE.NAME == sample.col.label]
+  folder = key$SANGER.SAMPLE.ID[key$SUPPLIER.SAMPLE.NAME == sample.col.label]
+  folder = file.path(folder, inside)
+  print(sprintf("Loading sample %s from %s", prelabel.a, folder))
+  seurat.obj.a = tryCatch({
+    Read10X(folder)
+  }, error = function(e){
+    Read10X(file.path(unlist(strsplit(folder, '/'))[1], unlist(strsplit(folder, '/'))[2]))
+  })
+  colnames(seurat.obj.a) = paste(prelabel.a, colnames(seurat.obj.a), sep = "_")
+  seurat.obj = CreateSeuratObject(raw.data = seurat.obj.a, min.cells = 0, min.genes = 0, project = "")
+  # if there is only one folder to read data from than add the prelabel to the cell names and return the objec
+  if (nfolders == 1){
+    return(seurat.obj)
+  }
+  # if more the 1 folder load next folder(s)
+  for (i in 2:nfolders){
+    sample.col.label = folders[i]
+    print(sample.col.label)
+    prelabel.b = key$Prelabel[key$SUPPLIER.SAMPLE.NAME == sample.col.label]
+    folder = key$SANGER.SAMPLE.ID[key$SUPPLIER.SAMPLE.NAME == sample.col.label]
+    folder = file.path(folder, inside)
+    print(sprintf("Loading sample %s from %s", prelabel.b, folder))
+    seurat.obj.b = tryCatch({
+       Read10X(folder)
+    }, error = function(e){
+      Read10X(file.path(unlist(strsplit(folder, '/'))[1], unlist(strsplit(folder, '/'))[2]))
+    })
+    seurat.obj = AddSamples(object=seurat.obj, new.data=seurat.obj.b, project=ProjectName, min.cells=0, min.genes=0, do.normalize=F, do.scale=F, do.center=F, add.cell.id=prelabel.b)
+    print(seurat.obj)
+  }
+  # eliminate the multiple underscore heading from the cell names introduces by sequential merging
+  cell.names = strsplit(colnames(seurat.obj@raw.data), "_")
+  cell.names = lapply(cell.names, function(x)x[x != ""])
+  cell.names = unlist(lapply(cell.names, FUN=function(parts){return(paste(parts, collapse="_"))}))
+  colnames(seurat.obj@data) = cell.names
+  colnames(seurat.obj@raw.data) = cell.names
+  names(seurat.obj@ident) = cell.names
+  return(seurat.obj)
+}
+
+# function to add meta data to a seurat object based on parsing cell names
+# currently it adds: fetal ids, sort ids, tissue, lane, stage and sample type
+add.meta.data = function(seurat.obj, sample.key, key){
+  cell.names = strsplit(colnames(seurat.obj@data), "_")
+  fetal.ids  = as.factor(unlist(lapply(cell.names, "[", 1)))
+  tissue     = as.factor(unlist(lapply(cell.names, "[", 2)))
+  sort.ids   = as.factor(unlist(lapply(cell.names, "[", 3)))
+  lanes      = as.factor(unlist(lapply(cell.names, "[", 4)))
+  key.key = which(sample.key$Sample %in% levels(fetal.ids))
+  # map stages
+  stages = plyr::mapvalues(x=fetal.ids, from=sample.key$Sample[key.key], to = sample.key$Stage[key.key])
+  # map sample type
+  sample.type = plyr::mapvalues(x=fetal.ids, from=sample.key$Sample[key.key], to = sample.key$Type[key.key])
+  # map fetal ids
+  fetal.ids = plyr::mapvalues(x=fetal.ids, from=sample.key$Sample[key.key], to = sample.key$Name[key.key])
+  # create gender vector
+  gender = strsplit(as.vector(fetal.ids), "_")
+  gender = as.factor(unlist(lapply(gender, "[",2)))
+  # create the AnnatomicalPart vector
+  unique.lanes = as.vector(unique(lanes))
+  unique.key = key[key$SANGER.SAMPLE.ID %in% unique.lanes, ]
+  AnnatomicalPart = plyr::mapvalues(x=lanes, from=unique.key$SANGER.SAMPLE.ID, to=unique.key$AnnatomicalPart)
+  # add the meta data
+  seurat.obj@meta.data$fetal.ids       = fetal.ids
+  seurat.obj@meta.data$sort.ids        = sort.ids 
+  seurat.obj@meta.data$tissue          = tissue
+  seurat.obj@meta.data$lanes           = lanes
+  seurat.obj@meta.data$stages          = stages
+  seurat.obj@meta.data$sample.type     = sample.type 
+  seurat.obj@meta.data$gender          = gender
+  seurat.obj@meta.data$AnnatomicalPart = AnnatomicalPart 
+  return(seurat.obj)
+}
+
+# function to perform filtering on a seurat object
+# this ensures all the datasets in a project are filtered with same criteria
+filter.seurat = function(seurat.obj, min.cells = 3, min.genes = 200, project.name = "", mito.genes.treshold = .2){
+  # apply filtering based on min.genes and min.cells
+  print("Filtering on cell and gene numbers ... ")
+  seurat.obj = CreateSeuratObject(raw.data = seurat.obj@raw.data, min.cells = min.cells, 
+                                   min.genes = min.genes, project = "")
+  seurat.obj.meta.data = seurat.obj@meta.data
+  saveRDS(seurat.obj.meta.data, file.path(output_folder, 'meta_data_mingenes.RDS'))
+  # calculate percentage of mitocondrial genes
+  mito.genes = grep(pattern = "^MT-", x = rownames(x = seurat.obj@data), value = TRUE)
+  percent.mito = Matrix::colSums(seurat.obj@raw.data[mito.genes, ])/Matrix::colSums(seurat.obj@raw.data)
+  seurat.obj = AddMetaData(object = seurat.obj, metadata = percent.mito, col.name = "percent.mito")
+  # filter on mitocondrial genes > mito.genes.treshold
+  print("Filtering on mitochondrial genes")
+  seurat.obj = FilterCells(object = seurat.obj, subset.names = c("percent.mito"), low.thresholds = c(-Inf), 
+                            high.thresholds = c(mito.genes.treshold))
+  seurat.obj.meta.data = seurat.obj@meta.data
+  saveRDS(seurat.obj.meta.data, file.path(output_folder, 'meta_data_mitogenes.RDS'))
+  return(seurat.obj)
+}
+
+# load the key
+# then do View(key) to look for the datasets you required
+# write the names of interest from key$V2 into data.folders
+# you can now access the folder names that need to be uploaded for creating a seurat object with required data
+#key = read.csv(file = key.fname, stringsAsFactors = FALSE, header=T)
+key = read.csv(file = key.fname, stringsAsFactors = FALSE, header=T, sep="\t")
+key$Fetus = unlist(regmatches(key$SUPPLIER.SAMPLE.NAME, gregexpr(pattern="F[0-9]{2}", text=key$SUPPLIER.SAMPLE.NAME)))
+key = key[key$Organ != "other", ]
+
+
+# load sample key
+sample.key = read.csv(sample.key.fname, stringsAsFactors = F, sep = "\t")
+# check all sample names in the key are also in the sample key:
+print(paste("All sample names in the key are also in the sample_key: ", all(key$Fetus %in% sample.key$Sample), sep = ""))
+
+# create prelabel column in key data frame
+# the prelabel is attached to the cell names before each barcode
+prelabel = paste(key$Fetus, key$Organ, sep = "_")
+# the first 20 supplier labels have 4 fields so the gate field is at position 4
+gate = strsplit(key$SUPPLIER.SAMPLE.NAME, split="_")
+gate = as.vector(unlist(lapply(gate, "[", 3)))
+gate = plyr::mapvalues(x = gate, from = c("CD45P", "CD45N", "TOT"), 
+                        to = c("CD45+", "CD45-", "Total"))
+prelabel = paste(prelabel, gate, sep = "_")
+prelabel = paste(prelabel, key$SANGER.SAMPLE.ID, sep = "_")
+key$Prelabel = prelabel
+
+# next the data can be parsed by tissue
+
+##########################################################################################
+##########################################################################################
+##########################################################################################
+
+data.folders = key$SUPPLIER.SAMPLE.NAME[(key$Organ == organ & key$Sequencing %in% sequencing.types ) & key$Passed]
+key = key[(key$Organ == organ & key$Sequencing %in% sequencing.types ) & key$Passed, ]
+print("Next is the key: ")
+print(key)
+
+# load the data
+cur.dir = getwd()
+setwd("../../data/sc_count_matrices/")
+seurat.obj = load_10x_data_from_folders(folders=data.folders, key = key, sample.key = sample.key)
+setwd(cur.dir)
+
+# parse meta data from cell names
+seurat.obj = add.meta.data(seurat.obj, sample.key = sample.key, key = key)
+print("Number of cells by lanes and gates before filtering:")
+print(table(seurat.obj@meta.data$fetal.ids, seurat.obj@meta.data$sort.ids))
+
+print('Cells by samples and gates before filtering:')
+print(table(seurat.obj@meta.data$fetal.ids, seurat.obj@meta.data$sort.ids))
+
+# apply filtering
+seurat.obj = filter.seurat(seurat.obj=seurat.obj, project.name="")
+# parse meta data from cell names because meta.data is lost during filtering
+seurat.obj = add.meta.data(seurat.obj, sample.key = sample.key, key=key)
+
+print('Cells by samples and gates after filtering:')
+print(table(seurat.obj@meta.data$fetal.ids, seurat.obj@meta.data$sort.ids))
+
+print('Cells by lanes and gates after filtering:')
+print(table(seurat.obj@meta.data$lanes, seurat.obj@meta.data$sort.ids))
+
+# normaliza data
+print("Normalizing data ...")
+seurat.obj = NormalizeData(object = seurat.obj, normalization.method = "LogNormalize", scale.factor = 10000)
+
+print("Computing variable genes ...")
+# find variable genes
+seurat.obj = FindVariableGenes(object = seurat.obj, mean.function = ExpMean, 
+                                        dispersion.function = LogVMR, x.low.cutoff = .0125, 
+                                        x.high.cutoff = 3, y.cutoff = .625)
+# calculate percentage of variable genes
+print(paste("Percentage of variable genes:", round(100 * length(seurat.obj@var.genes) / dim(seurat.obj@data)[1], digits = 2), sep = " "))
+
+# scale data in variable genes, otherwise pca is not possible
+print("Scaling data ...")
+seurat.obj = ScaleData(object=seurat.obj)
+
+# run PCA
+print("Performing PCA ...")
+seurat.obj = RunPCA(object = seurat.obj, pc.genes = seurat.obj@var.genes, do.print = FALSE, pcs.print = 1:20, genes.print = 10)
+
+# run TSNE
+print("Performing TSNE")
+seurat.obj = RunTSNE(object=seurat.obj, dims.use=1:20, seed.use=42, do.fast=TRUE)
+
+# run umap
+print("running UMAP")
+umap.coordinates = RunUMAP(pca.df=seurat.obj@dr$pca@cell.embeddings, tool_addr=tool_addr, python.addr=python.addr)
+rownames(umap.coordinates) = names(seurat.obj@ident)
+seurat.obj = SetDimReduction(object=seurat.obj, reduction.type="umap", slot="cell.embeddings", new.data=as.matrix(umap.coordinates))
+seurat.obj = SetDimReduction(object=seurat.obj, reduction.type="umap", slot="key", new.data="umap")
+
+# run force-directed graph
+print("Running force directed graph")
+seurat.obj = BuildSNN(object=seurat.obj, reduction.type="pca", dims.use=1:20, plot.SNN=F)
+fdg_coordinates = runFDG(pca.df=seurat.obj@dr$pca@cell.embeddings, snn=seurat.obj@snn, iterations=600, tool_addr=tool_addr, python.addr=python.addr)
+seurat.obj = SetDimReduction(object=seurat.obj, reduction.type="fdg", slot="cell.embeddings", new.data=as.matrix(fdg_coordinates))
+seurat.obj = SetDimReduction(object=seurat.obj, reduction.type="fdg", slot = "key", new.data = "fdg")
+
+if(annotate.cells){
+  print("Annotating cells ... ")
+  seurat.obj@meta.data$cell.labels = Apply_Classifier_On_Seurat_Object(seurat.obj=seurat.obj, classifier.fname=cell.type.SVM, tool_addr=tool_addr, python.addr=python.addr)
+}
+
+if (identify.doublets){
+  print("identifying doublets")
+  seurat.obj@meta.data$doublets = Apply_Classifier_On_Seurat_Object(seurat.obj=seurat.obj, classifier.fname=doublet.svm, tool_addr=tool_addr, python.addr=python.addr)
+}
+
+print("saving data")
+saveRDS(seurat.obj, save.at)
+
+
+if (identify.doublets){
+  print("Doublets and singlets: ")
+  print(table(seurat.obj@meta.data$fetal.ids, seurat.obj@meta.data$doublets)) 
+}
+
+print("Ended beautifully ... ")
+

pipelines/01_seurat_from_count_tables/seurat_from_count_tables.sh

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+#!/bin/bash
+
+#$ -cwd
+#$ -N seurat_from_count_tables
+#$ -V
+#$ -l h_rt=23:59:59
+#$ -l h_vmem=300G
+
+if [ "$#" -ne 1 ]; then
+    echo "Illegal number of parameters"
+    exit 1
+fi
+
+Rscript seurat_from_count_tables.R $1
+
+echo "End on `date`"