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Merge pull request #2540 from bebatut/goseq_extension
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GOSeq: update and add extraction of DE genes for categories
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@mvdbeek
mvdbeek committed Sep 6, 2019
2 parents be01ed9 + 57918e0 commit 8e19f8b
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220 changes: 119 additions & 101 deletions tools/goseq/goseq.r
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Expand Up @@ -10,47 +10,32 @@ suppressPackageStartupMessages({
library("ggplot2")
})

sessionInfo()

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 sampling distribution."),
make_option(c("-n","--nobias_tab"), type="character", default=FALSE, help="Path to output file with P-values estimated using hypergeometric 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?"),
make_option(c("-fc", "--fetch_cats"), default=NULL, type="character", help="Categories to get can include one or more of GO:CC, GO:BP, GO:MF, KEGG"),
make_option(c("-rd", "--rdata"), default=NULL, type="character", help="Path to RData output file."),
make_option(c("-tp", "--top_plot"), default=NULL, type="logical", help="Output PDF with top10 over-rep GO terms?")
make_option(c("-lf", "--length_file"), type="character", default=NULL, help="Path to tabular file mapping gene id to length"),
make_option(c("-g", "--genome"), type="character", default=NULL, help="Genome [used for looking up correct gene length]"),
make_option(c("-i", "--gene_id"), type="character", default=NULL, help="Gene ID format of genes in DGE file"),
make_option(c("-fc", "--fetch_cats"), type="character", default=NULL, help="Categories to get can include one or more of GO:CC, GO:BP, GO:MF, KEGG"),
make_option(c("-cat_file", "--category_file"), type="character", default=NULL, help="Path to tabular file with gene_id <-> category mapping"),
make_option(c("-w","--wallenius_tab"), type="character", default=NULL, help="Path to output file with P-values estimated using wallenius distribution"),
make_option(c("-n","--nobias_tab"), type="character", default=NULL, help="Path to output file with P-values estimated using hypergeometric distribution and no correction for gene length bias"),
make_option(c("-r", "--repcnt"), type="integer", default=0, help="Number of repeats for sampling"),
make_option(c("-s","--sampling_tab"), type="character", default=NULL, help="Path to output file with P-values estimated using sampling distribution"),
make_option(c("-p", "--p_adj_method"), type="character", default="BH", help="Multiple hypothesis testing correction method to use"),
make_option(c("-cat", "--use_genes_without_cat"), type="logical", default=FALSE, 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("-tp", "--top_plot"), type="character", default=NULL, help="Path to output PDF with top10 over-rep GO terms"),
make_option(c("-plots", "--make_plots"), default=FALSE, type="logical", help="Produce diagnostic plots?"),
make_option(c("-l","--length_bias_plot"), type="character", default=NULL, help="Path to length-bias plot"),
make_option(c("-sw","--sample_vs_wallenius_plot"), type="character", default=NULL, help="Path to plot comparing sampling with wallenius p-values"),
make_option(c("-rd", "--rdata"), type="character", default=NULL, help="Path to RData output file"),
make_option(c("-g2g", "--categories_genes_out_fp"), type="character", default=NULL, help="Path to file with categories (GO/KEGG terms) and associated DE genes")
)

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
rdata = args$rdata

if (!is.null(args$fetch_cats)) {
fetch_cats = unlist(strsplit(args$fetch_cats, ","))
} else {
Expand All @@ -59,101 +44,115 @@ if (!is.null(args$fetch_cats)) {

# format DE genes into named vector suitable for goseq
# check if header is present
first_line = read.delim(dge_file, header = FALSE, nrow=1)
first_line = read.delim(args$dge_file, header=FALSE, nrow=1)
second_col = toupper(first_line[, ncol(first_line)])
if (second_col == TRUE || second_col == FALSE) {
dge_table = read.delim(dge_file, header = FALSE, sep="\t")
dge_table = read.delim(args$dge_file, header=FALSE, sep="\t")
} else {
dge_table = read.delim(dge_file, header = TRUE, sep="\t")
dge_table = read.delim(args$dge_file, header=TRUE, sep="\t")
}
genes = as.numeric(as.logical(dge_table[,ncol(dge_table)])) # Last column contains TRUE/FALSE
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(length_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)
}
first_line = read.delim(args$length_file, header=FALSE, nrow=1)
if (is.numeric(first_line[, ncol(first_line)])) {
length_table = read.delim(args$length_file, header=FALSE, sep="\t", check.names=FALSE)
} else {
length_table = read.delim(args$length_file, header=TRUE, sep="\t", check.names=FALSE)
}
row.names(length_table) = length_table[,1]
# get vector of gene length in same order as the genes
gene_lengths = length_table[names(genes),][, ncol(length_table)]

# Estimate PWF

if (make_plots != 'false') {
pdf(length_bias_plot)
if (args$make_plots) {
pdf(args$length_bias_plot)
}
pwf=nullp(genes, genome = genome, id = gene_id, bias.data = gene_lengths, plot.fit=make_plots)
if (make_plots != 'false') {
pwf=nullp(genes, genome=args$genome, id=args$gene_id, bias.data=gene_lengths, plot.fit=args$make_plots)
if (args$make_plots) {
dev.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=fetch_cats)
} else {
if (is.null(args$category_file)) {
go_map=getgo(genes=names(genes), genome=args$genome, id=args$gene_id, fetch.cats=fetch_cats)
} else {
# check for header: first entry in first column must be present in genes, else it's a header
first_line = read.delim(category_file, header = FALSE, nrow=1)
first_line = read.delim(args$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)
}
go_map = read.delim(args$category_file, header=FALSE)
} else {
go_map = read.delim(args$category_file, header=TRUE)
}
}

results <- list()

# wallenius approximation of p-values
if (wallenius_tab != FALSE) {
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, args$wallenius_tab, sep="\t", row.names = FALSE, quote = FALSE)
results[['Wallenius']] <- GO.wall
runGoseq <- function(pwf, genome, gene_id, goseq_method, use_genes_without_cat, repcnt, gene2cat, p_adj_method, out_fp){
out=goseq(pwf, genome=genome, id=gene_id, method=goseq_method, use_genes_without_cat=use_genes_without_cat, gene2cat=go_map)
out$p.adjust.over_represented = p.adjust(out$over_represented_pvalue, method=p_adj_method)
out$p.adjust.under_represented = p.adjust(out$under_represented_pvalue, method=p_adj_method)
write.table(out, out_fp, sep="\t", row.names=FALSE, quote=FALSE)
return(out)
}

# wallenius approximation of p-values
if (!is.null(args$wallenius_tab)) results[['Wallenius']] <- runGoseq(
pwf,
genome=args$genome,
gene_id=args$gene_id,
goseq_method="Wallenius",
use_genes_without_cat=args$use_genes_without_cat,
repcnt=args$repcnt,
gene2cat=go_map,
p_adj_method=args$p_adj_method,
out_fp=args$wallenius_tab)


# hypergeometric (no length bias correction)
if (nobias_tab != FALSE) {
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, args$nobias_tab, sep="\t", row.names = FALSE, quote = FALSE)
results[['Hypergeometric']] <- GO.nobias
}
if (!is.null(args$nobias_tab)) results[['Hypergeometric']] <- runGoseq(
pwf,
genome=args$genome,
gene_id=args$gene_id,
goseq_method="Hypergeometric",
use_genes_without_cat=args$use_genes_without_cat,
repcnt=args$repcnt,
gene2cat=go_map,
p_adj_method=args$p_adj_method,
out_fp=args$nobias_tab)

# Sampling distribution
if (repcnt > 0) {

# capture the sampling progress so it doesn't fill stdout
zz <- file("/dev/null", open = "wt")
sink(zz)
GO.samp=goseq(pwf, genome = genome, id = gene_id, method="Sampling", repcnt=repcnt, use_genes_without_cat = use_genes_without_cat, gene2cat=go_map)
sink()
if (args$repcnt > 0){
results[['Sampling']] <- runGoseq(
pwf,
genome=args$genome,
gene_id=args$gene_id,
goseq_method="Sampling",
use_genes_without_cat=args$use_genes_without_cat,
repcnt=args$repcnt,
gene2cat=go_map,
p_adj_method=args$p_adj_method,
out_fp=args$sampling_tab)

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)
dev.off()
if (args$make_plots & !is.null(args$wallenius_tab)) {
pdf(args$sample_vs_wallenius_plot)
plot(log10(results[['Wallenius']][,2]),
log10(results[['Sampling']][match(results[['Sampling']][,1], results[['Wallenius']][,1]), 2]),
xlab="log10(Wallenius p-values)",
ylab="log10(Sampling p-values)",
xlim=c(-3,0))
abline(0,1,col=3,lty=2)
dev.off()
}
results[['Sampling']] <- GO.samp
}

# Plot the top 10
if (!is.null(args$top_plot)) {
cats_title <- gsub("GO:","", args$fetch_cats)
# modified from https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html
pdf("top10.pdf")
pdf(args$top_plot)
for (m in names(results)) {
p <- results[[m]] %>%
top_n(10, wt=-over_represented_pvalue) %>%
Expand All @@ -165,16 +164,35 @@ if (!is.null(args$top_plot)) {
geom_point() +
expand_limits(x=0) +
labs(x="% DE in category", y="Category", colour="Adj P value", size="Count", title=paste("Top over-represented categories in", cats_title), subtitle=paste(m, " method")) +
theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5))
theme(plot.title=element_text(hjust = 0.5), plot.subtitle=element_text(hjust = 0.5))
print(p)
}
dev.off()
}

# Extract the genes to the categories (GO/KEGG terms)
if (!is.null(args$categories_genes_out_fp)) {
cat2gene = split(rep(names(go_map), sapply(go_map, length)), unlist(go_map, use.names = FALSE))
# extract categories (GO/KEGG terms) for all results
categories = c()
for (m in names(results)) {
categories = c(categories, results[[m]]$category)
}
categories = unique(categories)
# extract the DE genes for each catge term
categories_genes = data.frame(Categories=categories, DEgenes=rep('', length(categories)))
categories_genes$DEgenes = as.character(categories_genes$DEgenes)
rownames(categories_genes) = categories
for (cat in categories){
tmp = pwf[cat2gene[[cat]],]
tmp = rownames(tmp[tmp$DEgenes > 0, ])
categories_genes[cat, 'DEgenes'] = paste(tmp, collapse=',')
}
# output
write.table(categories_genes, args$categories_genes_out_fp, sep = "\t", row.names=FALSE, quote=FALSE)
}

# Output RData file
if (!is.null(args$rdata)) {
save.image(file = "goseq_analysis.RData")
save.image(file=args$rdata)
}


sessionInfo()

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