CI
93ac6aad2e
Build pipeline: viash-hub.rnaseq.main-nn8dl
Source commit: 0c8a7eb648
Source message: remove citation
142 lines
5.4 KiB
R
Executable File
142 lines
5.4 KiB
R
Executable File
#!/usr/bin/env Rscript
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# Script for importing and processing transcript-level quantifications.
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# Written by Lorena Pantano, later modified by Jonathan Manning, and released under the MIT license.
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# Loading required libraries
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library(SummarizedExperiment)
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library(tximport)
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# Parsing command line arguments
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args <- commandArgs(trailingOnly=TRUE)
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if (length(args) < 4) {
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stop("Usage: tximport.r <coldata_path> <path> <prefix> <quant_type> <tx2gene_path>",
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call.=FALSE)
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}
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# Assigning command line arguments to variables
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coldata_path <- args[1]
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path <- args[2]
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prefix <- args[3]
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quant_type <- args[4]
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tx2gene_path <- args[5]
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## Functions
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# Build a table from a SummarizedExperiment object
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build_table <- function(se.obj, slot) {
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cbind(rowData(se.obj)[,1:2], assays(se.obj)[[slot]])
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}
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# Write a table to a file with given parameters
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write_se_table <- function(params) {
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file_name <- paste0(prefix, ".", params$suffix)
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write.table(build_table(params$obj, params$slot), file_name,
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sep="\t", quote=FALSE, row.names = FALSE)
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}
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# Read transcript metadata from a given path
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read_transcript_info <- function(tinfo_path){
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info <- file.info(tinfo_path)
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if (info$size == 0) {
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stop("tx2gene file is empty")
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}
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transcript_info <- read.csv(tinfo_path, sep="\t", header = FALSE,
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col.names = c("tx", "gene_id", "gene_name"))
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extra <- setdiff(rownames(txi[[1]]), as.character(transcript_info[["tx"]]))
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transcript_info <- rbind(transcript_info, data.frame(tx=extra, gene_id=extra, gene_name=extra))
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transcript_info <- transcript_info[match(rownames(txi[[1]]), transcript_info[["tx"]]), ]
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rownames(transcript_info) <- transcript_info[["tx"]]
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list(transcript = transcript_info,
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gene = unique(transcript_info[,2:3]),
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tx2gene = transcript_info[,1:2])
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}
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# Read and process sample/column data from a given path
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read_coldata <- function(coldata_path){
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if (file.exists(coldata_path)) {
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coldata <- read.csv(coldata_path, sep="\t")
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coldata <- coldata[match(names, coldata[,1]),]
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coldata <- cbind(files = fns, coldata)
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} else {
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message("ColData not available: ", coldata_path)
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coldata <- data.frame(files = fns, names = names)
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}
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rownames(coldata) <- coldata[["names"]]
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}
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# Create a SummarizedExperiment object with given data
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create_summarized_experiment <- function(counts, abundance, length, col_data, row_data) {
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SummarizedExperiment(assays = list(counts = counts, abundance = abundance, length = length),
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colData = col_data,
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rowData = row_data)
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}
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# Main script starts here
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# Define pattern for file names based on quantification type
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pattern <- ifelse(quant_type == "kallisto", "abundance.tsv", ".*quant_results\\.sf")
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fns <- list.files(path, pattern = pattern, recursive = T, full.names = T)
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names <- basename(fns)
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names(fns) <- names
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dropInfReps <- quant_type == "kallisto"
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# Import transcript-level quantifications
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txi <- tximport(fns, type = quant_type, txOut = TRUE, dropInfReps = dropInfReps)
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# Read transcript and sample data
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transcript_info <- read_transcript_info(tx2gene_path)
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coldata <- read_coldata(coldata_path)
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# Create initial SummarizedExperiment object
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se <- create_summarized_experiment(txi[["counts"]], txi[["abundance"]], txi[["length"]],
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DataFrame(coldata), transcript_info$transcript)
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# Setting parameters for writing tables
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params <- list(
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list(obj = se, slot = "abundance", suffix = "transcript_tpm.tsv"),
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list(obj = se, slot = "counts", suffix = "transcript_counts.tsv"),
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list(obj = se, slot = "length", suffix = "transcript_lengths.tsv")
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)
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# Process gene-level data if tx2gene mapping is available
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if ("tx2gene" %in% names(transcript_info) && !is.null(transcript_info$tx2gene)) {
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tx2gene <- transcript_info$tx2gene
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gi <- summarizeToGene(txi, tx2gene = tx2gene)
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gi.ls <- summarizeToGene(txi, tx2gene = tx2gene, countsFromAbundance = "lengthScaledTPM")
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gi.s <- summarizeToGene(txi, tx2gene = tx2gene, countsFromAbundance = "scaledTPM")
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gene_info <- transcript_info$gene[match(rownames(gi[[1]]), transcript_info$gene[["gene_id"]]),]
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rownames(gene_info) <- gene_info[["tx"]]
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col_data_frame <- DataFrame(coldata)
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# Create gene-level SummarizedExperiment objects
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gse <- create_summarized_experiment(gi[["counts"]], gi[["abundance"]], gi[["length"]],
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col_data_frame, gene_info)
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gse.ls <- create_summarized_experiment(gi.ls[["counts"]], gi.ls[["abundance"]], gi.ls[["length"]],
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col_data_frame, gene_info)
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gse.s <- create_summarized_experiment(gi.s[["counts"]], gi.s[["abundance"]], gi.s[["length"]],
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col_data_frame, gene_info)
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params <- c(params, list(
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list(obj = gse, slot = "length", suffix = "gene_lengths.tsv"),
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list(obj = gse, slot = "abundance", suffix = "gene_tpm.tsv"),
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list(obj = gse, slot = "counts", suffix = "gene_counts.tsv"),
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list(obj = gse.ls, slot = "abundance", suffix = "gene_tpm_length_scaled.tsv"),
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list(obj = gse.ls, slot = "counts", suffix = "gene_counts_length_scaled.tsv"),
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list(obj = gse.s, slot = "abundance", suffix = "gene_tpm_scaled.tsv"),
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list(obj = gse.s, slot = "counts", suffix = "gene_counts_scaled.tsv")
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))
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}
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# Writing tables for each set of parameters
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done <- lapply(params, write_se_table)
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# Output session information and citations
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# Removed for now because the 'tximeta' package is not found sometimes
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# citation("tximeta")
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sessionInfo() |