Mercurial > repos > iuc > goseq

annotate goseq.r @ 7:53eff3348302 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/goseq commit 8e19f8bcaea6f607a1eaa14bb88f2d625ed63df0"
author iuc
date Fri, 06 Sep 2019 07:50:23 -0400
parents 85f3b5ab2bf8
children 7c9731142105
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084d77515343 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/goseq commit b7dcd020c6a15fa55f392cc09cbc37580d6e75c4
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1 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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084d77515343 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/goseq commit b7dcd020c6a15fa55f392cc09cbc37580d6e75c4
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3 # we need that to not crash galaxy with an UTF8 error on German LC settings.
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4 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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6 suppressPackageStartupMessages({
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7 library("goseq")
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8 library("optparse")
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9 library("dplyr")
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10 library("ggplot2")
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11 })
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12
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13 sessionInfo()
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15 option_list <- list(
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16 make_option(c("-d", "--dge_file"), type="character", help="Path to file with differential gene expression result"),
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17 make_option(c("-lf", "--length_file"), type="character", default=NULL, help="Path to tabular file mapping gene id to length"),
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18 make_option(c("-g", "--genome"), type="character", default=NULL, help="Genome [used for looking up correct gene length]"),
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19 make_option(c("-i", "--gene_id"), type="character", default=NULL, help="Gene ID format of genes in DGE file"),
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20 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"),
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21 make_option(c("-cat_file", "--category_file"), type="character", default=NULL, help="Path to tabular file with gene_id <-> category mapping"),
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22 make_option(c("-w","--wallenius_tab"), type="character", default=NULL, help="Path to output file with P-values estimated using wallenius distribution"),
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23 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"),
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24 make_option(c("-r", "--repcnt"), type="integer", default=0, help="Number of repeats for sampling"),
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25 make_option(c("-s","--sampling_tab"), type="character", default=NULL, help="Path to output file with P-values estimated using sampling distribution"),
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26 make_option(c("-p", "--p_adj_method"), type="character", default="BH", help="Multiple hypothesis testing correction method to use"),
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27 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)."),
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28 make_option(c("-tp", "--top_plot"), type="character", default=NULL, help="Path to output PDF with top10 over-rep GO terms"),
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29 make_option(c("-plots", "--make_plots"), default=FALSE, type="logical", help="Produce diagnostic plots?"),
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30 make_option(c("-l","--length_bias_plot"), type="character", default=NULL, help="Path to length-bias plot"),
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31 make_option(c("-sw","--sample_vs_wallenius_plot"), type="character", default=NULL, help="Path to plot comparing sampling with wallenius p-values"),
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32 make_option(c("-rd", "--rdata"), type="character", default=NULL, help="Path to RData output file"),
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33 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")
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34 )
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36 parser <- OptionParser(usage = "%prog [options] file", option_list=option_list)
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37 args = parse_args(parser)
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38
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39 if (!is.null(args$fetch_cats)) {
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40 fetch_cats = unlist(strsplit(args$fetch_cats, ","))
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41 } else {
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42 fetch_cats = "Custom"
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43 }
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45 # format DE genes into named vector suitable for goseq
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46 # check if header is present
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47 first_line = read.delim(args$dge_file, header=FALSE, nrow=1)
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48 second_col = toupper(first_line[, ncol(first_line)])
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49 if (second_col == TRUE || second_col == FALSE) {
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50 dge_table = read.delim(args$dge_file, header=FALSE, sep="\t")
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51 } else {
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52 dge_table = read.delim(args$dge_file, header=TRUE, sep="\t")
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53 }
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54 genes = as.numeric(as.logical(dge_table[, ncol(dge_table)])) # Last column contains TRUE/FALSE
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55 names(genes) = dge_table[,1] # Assuming first column contains gene names
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57 # gene lengths, assuming last column
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58 first_line = read.delim(args$length_file, header=FALSE, nrow=1)
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59 if (is.numeric(first_line[, ncol(first_line)])) {
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60 length_table = read.delim(args$length_file, header=FALSE, sep="\t", check.names=FALSE)
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61 } else {
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62 length_table = read.delim(args$length_file, header=TRUE, sep="\t", check.names=FALSE)
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63 }
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64 row.names(length_table) = length_table[,1]
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65 # get vector of gene length in same order as the genes
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66 gene_lengths = length_table[names(genes),][, ncol(length_table)]
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68 # Estimate PWF
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69 if (args$make_plots) {
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70 pdf(args$length_bias_plot)
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71 }
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72 pwf=nullp(genes, genome=args$genome, id=args$gene_id, bias.data=gene_lengths, plot.fit=args$make_plots)
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73 if (args$make_plots) {
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74 dev.off()
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75 }
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77 # Fetch GO annotations if category_file hasn't been supplied:
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78 if (is.null(args$category_file)) {
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79 go_map=getgo(genes=names(genes), genome=args$genome, id=args$gene_id, fetch.cats=fetch_cats)
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80 } else {
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81 # check for header: first entry in first column must be present in genes, else it's a header
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82 first_line = read.delim(args$category_file, header=FALSE, nrow=1)
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83 if (first_line[,1] %in% names(genes)) {
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84 go_map = read.delim(args$category_file, header=FALSE)
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85 } else {
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86 go_map = read.delim(args$category_file, header=TRUE)
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87 }
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88 }
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89
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90 results <- list()
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91
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92 runGoseq <- function(pwf, genome, gene_id, goseq_method, use_genes_without_cat, repcnt, gene2cat, p_adj_method, out_fp){
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93 out=goseq(pwf, genome=genome, id=gene_id, method=goseq_method, use_genes_without_cat=use_genes_without_cat, gene2cat=go_map)
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94 out$p.adjust.over_represented = p.adjust(out$over_represented_pvalue, method=p_adj_method)
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95 out$p.adjust.under_represented = p.adjust(out$under_represented_pvalue, method=p_adj_method)
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96 write.table(out, out_fp, sep="\t", row.names=FALSE, quote=FALSE)
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97 return(out)
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98 }
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99
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100 # wallenius approximation of p-values
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101 if (!is.null(args$wallenius_tab)) results[['Wallenius']] <- runGoseq(
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102 pwf,
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103 genome=args$genome,
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104 gene_id=args$gene_id,
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105 goseq_method="Wallenius",
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106 use_genes_without_cat=args$use_genes_without_cat,
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107 repcnt=args$repcnt,
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108 gene2cat=go_map,
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109 p_adj_method=args$p_adj_method,
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110 out_fp=args$wallenius_tab)
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111
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112
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113 # hypergeometric (no length bias correction)
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114 if (!is.null(args$nobias_tab)) results[['Hypergeometric']] <- runGoseq(
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115 pwf,
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116 genome=args$genome,
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117 gene_id=args$gene_id,
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118 goseq_method="Hypergeometric",
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119 use_genes_without_cat=args$use_genes_without_cat,
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120 repcnt=args$repcnt,
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121 gene2cat=go_map,
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122 p_adj_method=args$p_adj_method,
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123 out_fp=args$nobias_tab)
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124
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125 # Sampling distribution
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126 if (args$repcnt > 0){
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127 results[['Sampling']] <- runGoseq(
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128 pwf,
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129 genome=args$genome,
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130 gene_id=args$gene_id,
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131 goseq_method="Sampling",
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132 use_genes_without_cat=args$use_genes_without_cat,
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133 repcnt=args$repcnt,
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134 gene2cat=go_map,
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135 p_adj_method=args$p_adj_method,
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136 out_fp=args$sampling_tab)
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137
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138 # Compare sampling with wallenius
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139 if (args$make_plots & !is.null(args$wallenius_tab)) {
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140 pdf(args$sample_vs_wallenius_plot)
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141 plot(log10(results[['Wallenius']][,2]),
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142 log10(results[['Sampling']][match(results[['Sampling']][,1], results[['Wallenius']][,1]), 2]),
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143 xlab="log10(Wallenius p-values)",
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144 ylab="log10(Sampling p-values)",
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145 xlim=c(-3,0))
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146 abline(0,1,col=3,lty=2)
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147 dev.off()
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148 }
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149 }
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150
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151 # Plot the top 10
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152 if (!is.null(args$top_plot)) {
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153 cats_title <- gsub("GO:","", args$fetch_cats)
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154 # modified from https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html
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155 pdf(args$top_plot)
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156 for (m in names(results)) {
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157 p <- results[[m]] %>%
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158 top_n(10, wt=-over_represented_pvalue) %>%
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159 mutate(hitsPerc=numDEInCat*100/numInCat) %>%
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160 ggplot(aes(x=hitsPerc,
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161 y=reorder(substr(term, 1, 40), -over_represented_pvalue), # only use 1st 40 chars of terms otherwise squashes plot
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162 colour=p.adjust.over_represented,
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163 size=numDEInCat)) +
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164 geom_point() +
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165 expand_limits(x=0) +
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166 labs(x="% DE in category", y="Category", colour="Adj P value", size="Count", title=paste("Top over-represented categories in", cats_title), subtitle=paste(m, " method")) +
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167 theme(plot.title=element_text(hjust = 0.5), plot.subtitle=element_text(hjust = 0.5))
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168 print(p)
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169 }
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170 dev.off()
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171 }
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172
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173 # Extract the genes to the categories (GO/KEGG terms)
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174 if (!is.null(args$categories_genes_out_fp)) {
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175 cat2gene = split(rep(names(go_map), sapply(go_map, length)), unlist(go_map, use.names = FALSE))
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176 # extract categories (GO/KEGG terms) for all results
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177 categories = c()
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178 for (m in names(results)) {
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179 categories = c(categories, results[[m]]$category)
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180 }
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181 categories = unique(categories)
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182 # extract the DE genes for each catge term
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183 categories_genes = data.frame(Categories=categories, DEgenes=rep('', length(categories)))
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184 categories_genes$DEgenes = as.character(categories_genes$DEgenes)
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185 rownames(categories_genes) = categories
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186 for (cat in categories){
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187 tmp = pwf[cat2gene[[cat]],]
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188 tmp = rownames(tmp[tmp$DEgenes > 0, ])
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189 categories_genes[cat, 'DEgenes'] = paste(tmp, collapse=',')
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190 }
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191 # output
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192 write.table(categories_genes, args$categories_genes_out_fp, sep = "\t", row.names=FALSE, quote=FALSE)
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193 }
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194
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195 # Output RData file
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196 if (!is.null(args$rdata)) {
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197 save.image(file=args$rdata)
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198 }