comparison goseq.r @ 0:ade933eff007 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/goseq commit b7dcd020c6a15fa55f392cc09cbc37580d6e75c4

0:ade933eff007

options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )

# we need that to not crash galaxy with an UTF8 error on German LC settings.
loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")

suppressPackageStartupMessages({
    library("goseq")
    library("optparse")
})

option_list <- list(
    make_option(c("-d", "--dge_file"), type="character", help="Path to file with differential gene expression result"),
    make_option(c("-w","--wallenius_tab"), type="character", help="Path to output file with P-values estimated using wallenius distribution."),
    make_option(c("-s","--sampling_tab"), type="character", default=FALSE, help="Path to output file with P-values estimated using wallenius distribution."),
    make_option(c("-n","--nobias_tab"), type="character", default=FALSE, help="Path to output file with P-values estimated using wallenius distribution and no correction for gene length bias."),
    make_option(c("-l","--length_bias_plot"), type="character", default=FALSE, help="Path to length-bias plot."),
    make_option(c("-sw","--sample_vs_wallenius_plot"), type="character", default=FALSE, help="Path to plot comparing sampling with wallenius p-values."),
    make_option(c("-r", "--repcnt"), type="integer", default=100, help="Number of repeats for sampling"),
    make_option(c("-lf", "--length_file"), type="character", default="FALSE", help = "Path to tabular file mapping gene id to length"),
    make_option(c("-cat_file", "--category_file"), default="FALSE", type="character", help = "Path to tabular file with gene_id <-> category mapping."),
    make_option(c("-g", "--genome"), default=NULL, type="character", help = "Genome [used for looking up correct gene length]"),
    make_option(c("-i", "--gene_id"), default=NULL, type="character", help = "Gene ID format of genes in DGE file"),
    make_option(c("-p", "--p_adj_method"), default="BH", type="character", help="Multiple hypothesis testing correction method to use"),
    make_option(c("-cat", "--use_genes_without_cat"), default=FALSE, type="logical",
                help="A large number of gene may have no GO term annotated. If this option is set to FALSE, genes without category will be ignored in the calculation of p-values(default behaviour). If TRUE these genes will count towards the total number of genes outside the tested category (default behaviour prior to version 1.15.2)."),
    make_option(c("-plots", "--make_plots"), default=FALSE, type="logical", help="produce diagnostic plots?")
    )

parser <- OptionParser(usage = "%prog [options] file", option_list=option_list)
args = parse_args(parser)

# Vars:
dge_file = args$dge_file
category_file = args$category_file
length_file = args$length_file
genome = args$genome
gene_id = args$gene_id
wallenius_tab = args$wallenius_tab
sampling_tab = args$sampling_tab
nobias_tab = args$nobias_tab
length_bias_plot = args$length_bias_plot
sample_vs_wallenius_plot = args$sample_vs_wallenius_plot
repcnt = args$repcnt
p_adj_method = args$p_adj_method
use_genes_without_cat = args$use_genes_without_cat
make_plots = args$make_plots

# format DE genes into named vector suitable for goseq
dge_table = read.delim(dge_file, header = FALSE, sep="\t")
genes = as.numeric(as.logical(dge_table[,ncol(dge_table)])) # Last column contains TRUE/FALSE
names(genes) = dge_table[,1] # Assuming first column contains gene names

# gene lengths, assuming last column
if (length_file != "FALSE" ) {
  first_line = read.delim(dge_file, header = FALSE, nrow=1)
  if (is.numeric(first_line[, ncol(first_line)])) {
    length_table = read.delim(length_file, header=FALSE, sep="\t", check.names=FALSE)
    } else {
    length_table = read.delim(length_file, header=TRUE, sep="\t", check.names=FALSE)
    }
  row.names(length_table) = length_table[,1]
  gene_lengths = length_table[names(genes),][,ncol(length_table)]
  } else {
  gene_lengths = getlength(names(genes), genome, gene_id)
  }

# Estimate PWF

if (make_plots == TRUE) {
  pdf(length_bias_plot)
}
pwf=nullp(genes, genome = genome, id = gene_id, bias.data = gene_lengths, plot.fit=make_plots)
graphics.off()

# Fetch GO annotations if category_file hasn't been supplied:
if (category_file == "FALSE") {
  go_map=getgo(genes = names(genes), genome = genome, id = gene_id, fetch.cats=c("GO:CC", "GO:BP", "GO:MF", "KEGG"))
  } else {
  # check for header: first entry in first column must be present in genes, else it's a header
  first_line = read.delim(category_file, header = FALSE, nrow=1)
  if (first_line[,1] %in% names(genes)) {
     go_map = read.delim(category_file, header = FALSE)
     } else {
     go_map = read.delim(category_file, header= TRUE)
    }
}

# wallenius approximation of p-values
if (wallenius_tab != "" && wallenius_tab!="None") {
  GO.wall=goseq(pwf, genome = genome, id = gene_id, use_genes_without_cat = use_genes_without_cat, gene2cat=go_map)
  GO.wall$p.adjust.over_represented = p.adjust(GO.wall$over_represented_pvalue, method=p_adj_method)
  GO.wall$p.adjust.under_represented = p.adjust(GO.wall$under_represented_pvalue, method=p_adj_method)
  write.table(GO.wall, wallenius_tab, sep="\t", row.names = FALSE, quote = FALSE)
}

# hypergeometric (no length bias correction)
if (nobias_tab != "" && nobias_tab != "None") {
  GO.nobias=goseq(pwf, genome = genome, id = gene_id, method="Hypergeometric", use_genes_without_cat = use_genes_without_cat, gene2cat=go_map)
  GO.nobias$p.adjust.over_represented = p.adjust(GO.nobias$over_represented_pvalue, method=p_adj_method)
  GO.nobias$p.adjust.under_represented = p.adjust(GO.nobias$under_represented_pvalue, method=p_adj_method)
  write.table(GO.nobias, nobias_tab, sep="\t", row.names = FALSE, quote = FALSE)
}

# Sampling distribution
if (repcnt > 0) {
  GO.samp=goseq(pwf, genome = genome, id = gene_id, method="Sampling", repcnt=repcnt, use_genes_without_cat = use_genes_without_cat, gene2cat=go_map)
  GO.samp$p.adjust.over_represented = p.adjust(GO.samp$over_represented_pvalue, method=p_adj_method)
  GO.samp$p.adjust.under_represented = p.adjust(GO.samp$under_represented_pvalue, method=p_adj_method)
  write.table(GO.samp, sampling_tab, sep="\t", row.names = FALSE, quote = FALSE)
  # Compare sampling with wallenius
  if (make_plots == TRUE) {
  pdf(sample_vs_wallenius_plot)
  plot(log10(GO.wall[,2]), log10(GO.samp[match(GO.samp[,1],GO.wall[,1]),2]),
     xlab="log10(Wallenius p-values)",ylab="log10(Sampling p-values)",
     xlim=c(-3,0))
     abline(0,1,col=3,lty=2)
  graphics.off()
  }
}

sessionInfo()