Mercurial > repos > artbio > gsc_filter_genes
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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_filter_genes commit b184054ad6d4230ab0a714c13f9ef32449faa327
author | artbio |
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date | Mon, 16 Oct 2023 23:26:20 +0000 |
parents | 5d2304b09f58 |
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# Filter out low expressed genes # Example of command (used for generate output file) : # Rscript filter_genes.R -f <input file> -o <output file> # load packages that are provided in the conda env options(show.error.messages = FALSE, error = function() { cat(geterrmessage(), file = stderr()) q("no", 1, FALSE) } ) loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") library(optparse) # Arguments option_list <- list( make_option( c("-f", "--input"), default = NA, type = "character", help = "Input file that contains count values to filter" ), make_option( c("-s", "--sep"), default = "\t", type = "character", help = "File separator [default : '%default' ]" ), make_option( c("-c", "--colnames"), default = TRUE, type = "logical", help = "first line is a header [default : '%default' ]" ), make_option( "--percentile_detection", default = 0, type = "numeric", help = "Include genes with detected expression in at least \ this fraction of cells [default : '%default' ]" ), make_option( "--absolute_detection", default = 0, type = "numeric", help = "Include genes with detected expression in at least \ this number of cells [default : '%default' ]" ), make_option( c("-o", "--output"), default = NA, type = "character", help = "Output name [default : '%default' ]" ) ) opt <- parse_args(OptionParser(option_list = option_list), args = commandArgs(trailingOnly = TRUE)) if (opt$sep == "tab") { opt$sep <- "\t" } if (opt$sep == "comma") { opt$sep <- "," } # Open files data.counts <- read.delim( opt$input, h = opt$colnames, row.names = 1, sep = opt$sep, check.names = FALSE ) # note the [if else] below, to handle percentile_detection=absolute_detection=0 # Search for genes that are expressed in a certain percent of cells if (opt$percentile_detection > 0) { kept_genes <- rowSums(data.counts != 0) >= (opt$percentile_detection * ncol(data.counts)) } else { # Search for genes that are expressed in more than an absolute number of cells kept_genes <- rowSums(data.counts != 0) >= (opt$absolute_detection) } # Filter matrix data.counts <- data.counts[kept_genes, ] data.counts <- cbind(Genes = rownames(data.counts), data.counts) # Save filtered matrix write.table( data.counts, opt$output, sep = "\t", quote = FALSE, col.names = TRUE, row.names = FALSE )