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goAna.R
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#!/usr/bin/env Rscript
suppressPackageStartupMessages(library("optparse"))
## parse command line arguments
option_list <- list(
make_option(c("-i", "--inFile"), help="input file containing gene list(s)"),
make_option(c("-o", "--outDir"), help="output directory to store pdf image file(s)"),
make_option(c("-e", "--genome"), help="genome for which to perform the analysis"),
make_option(c("-d", "--geneIdType"), default="SYMBOL", help="input gene id type (default=%default)"),
make_option(c("-a", "--annotation"), default="GOTERM_BP_ALL", help="annotation type (default=%default)"),
make_option(c("-p", "--pValue"), default="0.05", help="p-value (defaut=%default)"),
make_option(c("-m", "--maxClass"), default=20, help="maximum number of go classes to plot (default=%default)"),
make_option(c("-n", "--minGene"), default=10, help="minimum genes in a go class (default=%default)"),
make_option(c("-c", "--compareCluster"), help="input file contains multiple gene lists (one per column)", action="store_true"),
make_option(c("-f", "--formula"), help="input file contains multiple gene lists (column 1: genes; column 2: class)", action="store_true"),
make_option(c("-l", "--listAnnotation"), help="just list different GO annotation options", action="store_true"),
make_option(c("-s", "--sessionFile"), default="go_analysis.Rsession", help="output session file. It will be used, if already exist"),
make_option(c("-w", "--figWidth"), default="10", help="width of output figure"),
make_option(c("-t", "--figHeight"), default="20", help="height of output figure"),
make_option(c("-u", "--allowDuplicates"), default=0, help="Plot top -m classes even if some are common between classes"),
make_option(c("-b", "--bkgFile"), help="input file containing background gene list"),
make_option(c("-r", "--ftrResFile"), help="input file containing manually filtered GO categories to plot"),
make_option(c("-q", "--logCount"), help="plot count data in log", action="store_true")
)
parser <- OptionParser(usage = "%prog [options]", option_list=option_list)
opt <- parse_args(parser)
## check, if all required arguments are given
if((is.null(opt$inFile) | is.null(opt$genome) | is.null(opt$outDir)) & is.null(opt$listAnnotation)) {
cat("\nProgram: goAna.R (R script to plot go enrichment analysis)\n")
cat("Author: BRIC, University of Copenhagen, Denmark\n")
cat("Version: 1.0\n")
cat("Contact: pundhir@binf.ku.dk\n");
print_help(parser)
q()
}
## load libraries
suppressPackageStartupMessages(library(DOSE))
suppressPackageStartupMessages(library(ReactomePA))
suppressPackageStartupMessages(library(clusterProfiler))
suppressPackageStartupMessages(library(mygene))
suppressPackageStartupMessages(library(RDAVIDWebService))
suppressPackageStartupMessages(library(session))
suppressPackageStartupMessages(library(ggplot2))
##It may help to represent the p-values of enrichment as a transformation whereby you represent the, as -10*log(P-value)
#cat(sprintf("A: %s", opt$compareCluster));
#cat(sprintf("B: %s", opt$formula));
#q();
if(opt$genome=="mmu") {
genomeDb <- "org.Mm.eg.db"
genomeReactome <- "mouse"
} else {
genomeDb <- "org.Hs.eg.db"
genomeReactome <- "human"
}
if(!is.null(opt$listAnnotation)) {
david<-DAVIDWebService$new("pundhir@binf.ku.dk")
idType(genomeDb)
getIdTypes(david)
#getAllAnnotationCategoryNames(david)
## ?DAVIDWebService (for more details)
} else if(!is.null(opt$compareCluster)) {
## read input gene list file
gene <- read.table(opt$inFile, fill=TRUE, sep="\t")
opt$sessionFile <- sprintf("%s/go_analysis_compareCluster_%s.Rsession", opt$outDir, opt$annotation)
if(!is.null(opt$ftrResFile)) {
figWidth=opt$figWidth
figHeight=opt$figHeight
maxClass=opt$maxClass
minGene=opt$minGene
outDir=opt$outDir
allowDuplicates=opt$allowDuplicates
opt$figWidth=figWidth
opt$figHeight=figHeight
opt$maxClass=maxClass
opt$minGene=minGene
opt$outDir=outDir
opt$allowDuplicates <- allowDuplicates
ftr_results <- read.table(opt$ftrResFile, sep="\t", header=F)
data <- ftr_results
#data <- ftr_results[,c(1,2,3,4,5,6,7,8,10)]
colnames(data) <- c("Cluster", "V2", "ID", "Description", "GeneRatio", "BgRatio", "pvalue", "p.adjust", "qvalue", "geneID", "Count", "geneIDf")
} else if(!file.exists(opt$sessionFile)) {
## convert gene symbol to entrex gene id
gene[gene==""] <- NA
#geneList <- lapply(gene, function(x) { list <- queryMany(x[!is.na(x)], scopes=opt$geneIdType, fields="entrezgene", OrgDb=genomeDb)$entrezgene; list[which(!is.na(list))]; } )
geneList <- lapply(gene, function(x) { list <- bitr(x[!is.na(x)], fromType=opt$geneIdType, toType="ENTREZID", OrgDb=genomeDb)$ENTREZID; list[which(!is.na(list))]; } )
if(opt$geneIdType=="ENSEMBLTRANS") {
geneList <- unique(geneList[,c(2,3)])
}
## compute go enrichment
#opt$pValue <- 0.9
if(opt$annotation=="DAVID"){
results=compareCluster(geneList, fun="enrichDAVID", idType="ENTREZ_GENE_ID", listType="Gene", annotation="GOTERM_BP_ALL", david.user = "pundhir@binf.ku.dk", species=opt$genome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
} else if(opt$annotation=="KEGG_PATHWAY") {
results=compareCluster(geneList, fun="enrichKEGG", organism=opt$genome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
} else if(opt$annotation=="DISEASE_ONTOLOGY") {
results=compareCluster(geneList, fun="enrichDO", pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
} else if(opt$annotation=="REACTOME_PATHWAY") {
results=compareCluster(geneList, fun="enrichPathway", organism=genomeReactome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
} else {
results=compareCluster(geneList, fun="enrichGO", ont="BP", OrgDb=genomeDb, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
}
#data <- as.data.frame(results)[,c(1,2,3,4,5,6,7,8,10)]
data <- results@compareClusterResult[,c(1,3,4,5,6,7,8,9,10,11)]
} else {
figWidth=opt$figWidth
figHeight=opt$figHeight
maxClass=opt$maxClass
minGene=opt$minGene
outDir=opt$outDir
allowDuplicates=opt$allowDuplicates
load(opt$sessionFile)
opt$figWidth=figWidth
opt$figHeight=figHeight
opt$maxClass=maxClass
opt$minGene=minGene
opt$outDir=outDir
opt$allowDuplicates <- allowDuplicates
#data <- as.data.frame(results)[,c(1,2,3,4,5,6,7,8,10)]
data <- results@compareClusterResult[,c(1,3,4,5,6,7,8,9,10,11)]
}
#outFile <- sprintf("%s/go_analysis_compareCluster_%s.pdf", opt$outDir, opt$annotation)
#pdf(outFile)
#plot(results, includeAll=TRUE, showCategory=NULL)
#dev.off()
if(nrow(data)>=1) {
if(is.null(opt$ftrResFile)) {
outFile <- sprintf("%s/go_analysis_compareCluster_%s.pdf", opt$outDir, opt$annotation)
} else {
outFile <- sprintf("%s/go_analysis_compareCluster_filtered_%s.pdf", opt$outDir, opt$annotation)
}
data$GeneDensity <- apply(data, 1, function(x) as.numeric(unlist(strsplit(x[4],"/"))[1])/as.numeric(unlist(strsplit(x[4],"/"))[2]))
data$BgDensity <- apply(data, 1, function(x) as.numeric(unlist(strsplit(x[5],"/"))[1])/as.numeric(unlist(strsplit(x[5],"/"))[2]))
data$fc <- log2(data$GeneDensity/data$BgDensity)
if(as.numeric(opt$allowDuplicates)==0) {
data <- subset(data, !duplicated(ID))
}
data <- data[order(data$Cluster), ]
data_sig <- Reduce(rbind, by(data, data["Cluster"], head, n=as.numeric(opt$maxClass)))
if(as.numeric(opt$allowDuplicates)==1) {
data_sig <- data[data$Description %in% data_sig$Description,]
data_sig$Description <-factor(data_sig$Description, levels=unique(data_sig$Description))
} else {
data_sig$Description <-factor(data_sig$Description, levels=data_sig$Description)
}
if(is.null(opt$logCount)) {
p <- ggplot(data_sig, aes(x = Cluster, y = Description)) +
geom_point(aes(colour=pvalue,size=Count)) +
#geom_point(aes(colour=pvalue,size=GeneDensity*100)) +
scale_colour_gradient(low="brown", high="yellow") +
scale_size(range=c(1,10))
#scale_size(range=c((min(data_sig$GeneDensity)*100),(max(data_sig$GeneDensity*100))), breaks=waiver(), labels=waiver()) +
scale_size_area(breaks=seq(0,100,by=5), max_size=15) +
theme(text=element_text(size=10), axis.text.x=element_text(angle=90)) +
theme_bw(base_size=15)
} else {
p <- ggplot(data_sig, aes(x = Cluster, y = Description)) +
geom_point(aes(colour=pvalue,size=log(Count))) +
#geom_point(aes(colour=pvalue,size=GeneDensity*100)) +
scale_colour_gradient(low="brown", high="yellow") +
scale_size(range=c(1,10))
#scale_size(range=c((min(data_sig$GeneDensity)*100),(max(data_sig$GeneDensity*100))), breaks=waiver(), labels=waiver()) +
scale_size_area(breaks=seq(1,10,by=1), max_size=10) +
theme(text=element_text(size=10), axis.text.x=element_text(angle=90)) +
theme_bw(base_size=15)
}
#ggsave(p, dpi=300, height=as.numeric(opt$maxClass)*1.5, width=length(unique(data_sig$Cluster))*1.5, filename=outFile, useDingbats=FALSE)
ggsave(p, dpi=300, height=as.numeric(opt$figHeight), width=as.numeric(opt$figWidth), filename=outFile, useDingbats=FALSE)
#ggsave(p, dpi=300, height=20, width=10, filename=outFile, useDingbats=FALSE)
}
if(is.null(opt$ftrResFile)) {
outFile <- sprintf("%s/go_analysis_compareCluster_%s.xls", opt$outDir, opt$annotation)
#data <- as.data.frame(results@compareClusterResult)
#data$geneIDf <- apply(data, 1, function(y) paste(unlist(lapply(unlist(strsplit(y[9], "/")), function(x) geneList[which(geneList$ENTREZID==x),]$V1)), collapse="/"))
#write.table(data, file=outFile, sep="\t", quote=F, col.names=T, row.names=F)
write.table(as.data.frame(results@compareClusterResult), file=outFile, sep="\t", quote=F, col.names=T, row.names=F)
rm(figWidth, figHeight, maxClass, minGene, outDir, allowDuplicates)
save.session(opt$sessionFile)
}
} else if(!is.null(opt$formula)) {
## read input gene list file
geneList <- read.table(opt$inFile)
## read background gene list file
if(!is.null(opt$bkgFile)) {
bkgList <- read.table(opt$bkgFile)
}
opt$sessionFile <- sprintf("%s/go_analysis_compareClusterFormula_%s.Rsession", opt$outDir, opt$annotation)
if(!is.null(opt$ftrResFile)) {
figWidth=opt$figWidth
figHeight=opt$figHeight
maxClass=opt$maxClass
minGene=opt$minGene
outDir=opt$outDir
allowDuplicates=opt$allowDuplicates
opt$figWidth=figWidth
opt$figHeight=figHeight
opt$maxClass=maxClass
opt$minGene=minGene
opt$outDir=outDir
opt$allowDuplicates <- allowDuplicates
ftr_results <- read.table(opt$ftrResFile, sep="\t", header=F)
data <- ftr_results
#data <- ftr_results[,c(1,2,3,4,5,6,7,8,10)]
colnames(data) <- c("Cluster", "V2", "ID", "Description", "GeneRatio", "BgRatio", "pvalue", "p.adjust", "qvalue", "geneID", "Count", "geneIDf")
} else if(!file.exists(opt$sessionFile)) {
## convert gene symbol to entrex gene id
geneList_conv <- bitr(geneList$V1, fromType=opt$geneIdType, toType="ENTREZID", OrgDb=genomeDb)
if(!is.null(opt$bkgFile)) {
bkgList_conv <- bitr(bkgList$V1, fromType=opt$geneIdType, toType="ENTREZID", OrgDb=genomeDb)
}
geneList_conv <- as.data.frame(geneList_conv)
geneList <- merge(geneList, geneList_conv, by.x="V1", by.y=opt$geneIdType)
geneList <- geneList[which(!is.na(geneList$ENTREZID)),]
if(opt$geneIdType=="ENSEMBLTRANS") {
geneList <- unique(geneList[,c(2,3)])
}
if(!is.null(opt$bkgFile)) {
bkgList_conv <- as.data.frame(bkgList_conv)
bkgList <- merge(bkgList, bkgList_conv, by.x="V1", by.y=opt$geneIdType)
bkgList <- bkgList[which(!is.na(bkgList$ENTREZID)),]
if(opt$geneIdType=="ENSEMBLTRANS") {
bkgList <- unique(bkgList[,c(2,3)])
}
}
## compute go enrichment
if(opt$annotation=="DAVID"){
results=compareCluster(ENTREZID~V2, data=geneList, fun="enrichDAVID", idType="ENTREZ_GENE_ID", listType="Gene", annotation="GOTERM_BP_ALL", david.user = "pundhir@binf.ku.dk", species=opt$genome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
} else if(opt$annotation=="KEGG_PATHWAY"){
results=compareCluster(ENTREZID~V2, organism=opt$genome, data=geneList, fun="enrichKEGG", pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else if(opt$annotation=="DISEASE_ONTOLOGY"){
results=compareCluster(ENTREZID~V2, data=geneList, fun="enrichDO", pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else if(opt$annotation=="REACTOME_PATHWAY"){
results=compareCluster(ENTREZID~V2, organism=genomeReactome, data=geneList, fun="enrichPathway", pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else {
if(!is.null(opt$bkgFile)) {
results=compareCluster(ENTREZID~V2, data=geneList, fun="enrichGO", ont="BP", OrgDb=genomeDb, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), universe=bkgList$ENTREZID, minGSSize=as.numeric(opt$minGene))
## groupGO is not useful due to the reason that it does not give p-values
results_levels=compareCluster(ENTREZID~V2, data=geneList, fun="groupGO", ont="BP", OrgDb=genomeDb, universe=bkgList$ENTREZID, level=3)
} else {
results=compareCluster(ENTREZID~V2, data=geneList, fun="enrichGO", ont="BP", OrgDb=genomeDb, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
## groupGO is not useful due to the reason that it does not give p-values
results_levels=compareCluster(ENTREZID~V2, data=geneList, fun="groupGO", ont="BP", OrgDb=genomeDb, level=3)
}
}
#data <- as.data.frame(results)[,c(1,2,3,4,5,6,7,8,10)]
data <- results@compareClusterResult[,c(1,3,4,5,6,7,8,9,10,11)]
} else {
figWidth=opt$figWidth
figHeight=opt$figHeight
maxClass=opt$maxClass
minGene=opt$minGene
outDir=opt$outDir
allowDuplicates=opt$allowDuplicates
load(opt$sessionFile)
opt$figWidth=figWidth
opt$figHeight=figHeight
opt$maxClass=maxClass
opt$minGene=minGene
opt$outDir=outDir
opt$allowDuplicates=allowDuplicates
#opt$maxClass=10
#opt$outDir="go_analysis/pu1_pregm_wt_ko/all"
#data <- as.data.frame(results)[,c(1,2,3,4,5,6,7,8,10)]
data <- results@compareClusterResult[,c(1,3,4,5,6,7,8,9,10,11)]
}
if(nrow(data)>1) {
if(is.null(opt$ftrResFile)) {
outFile <- sprintf("%s/go_analysis_compareClusterFormula_%s.pdf", opt$outDir, opt$annotation)
} else {
outFile <- sprintf("%s/go_analysis_compareClusterFormula_filtered_%s.pdf", opt$outDir, opt$annotation)
}
library(ggplot2)
data$GeneDensity <- apply(data, 1, function(x) as.numeric(unlist(strsplit(x[4],"/"))[1])/as.numeric(unlist(strsplit(x[4],"/"))[2]))
data$BgDensity <- apply(data, 1, function(x) as.numeric(unlist(strsplit(x[5],"/"))[1])/as.numeric(unlist(strsplit(x[5],"/"))[2]))
data$fc <- log2(data$GeneDensity/data$BgDensity)
if(as.numeric(opt$allowDuplicates)==0) {
data <- subset(data, !duplicated(ID))
}
data <- data[order(data$Cluster), ]
data_sig <- Reduce(rbind, by(data, data["Cluster"], head, n=as.numeric(opt$maxClass)))
if(as.numeric(opt$allowDuplicates)==1) {
data_sig <- data[data$Description %in% data_sig$Description,]
data_sig$Description <-factor(data_sig$Description, levels=unique(data_sig$Description))
} else {
data_sig$Description <-factor(data_sig$Description, levels=data_sig$Description)
}
#high <- as.vector(quantile(data_sig$pvalue)[3])
#data_sig[which(data_sig$pvalue > high),]$pvalue <- high
data_sig$pvalue <- -log10(data_sig$pvalue)
if(is.null(opt$logCount)) {
p <- ggplot(data_sig, aes(x = Cluster, y = Description)) +
geom_point(aes(colour=pvalue,size=Count)) +
#geom_point(aes(colour=pvalue,size=GeneDensity*100)) +
#scale_colour_gradient(high="brown", low="yellow", name="p-value") +
#scale_colour_gradient(low="#00441b", high="#ccece6", name="p-value") +
scale_colour_gradient(high="#00441b", low="#99d8c9", name="-log10(p-value)") +
#breaks=c(1e-20, 1e-15, 1e-10, 1e-5, 0.01, 0.05)) +
#scale_size(range=c(1,10)) +
#scale_size(range=c((min(data_sig$GeneDensity)*100),(max(data_sig$GeneDensity*100))), breaks=waiver(), labels=waiver()) +
scale_size_area(breaks=seq(0,100,by=10), max_size=20) +
theme(text=element_text(size=10), axis.text.x=element_text(angle=90)) +
theme_bw(base_size=15)
} else {
p <- ggplot(data_sig, aes(x = Cluster, y = Description)) +
geom_point(aes(colour=pvalue,size=log(Count))) +
#geom_point(aes(colour=pvalue,size=GeneDensity*100)) +
#scale_colour_gradient(high="brown", low="yellow", name="p-value") +
#scale_colour_gradient(low="#00441b", high="#ccece6", name="p-value") +
scale_colour_gradient(high="#00441b", low="#99d8c9", name="-log10(p-value)") +
#breaks=c(1e-20, 1e-15, 1e-10, 1e-5, 0.01, 0.05)) +
#scale_size(range=c(1,10)) +
#scale_size(range=c((min(data_sig$GeneDensity)*100),(max(data_sig$GeneDensity*100))), breaks=waiver(), labels=waiver()) +
scale_size_area(breaks=seq(1,10,by=1), max_size=10) +
theme(text=element_text(size=10), axis.text.x=element_text(angle=90)) +
theme_bw(base_size=15)
}
#ggsave(p, dpi=300, height=as.numeric(opt$maxClass)*1.5, width=length(unique(data_sig$Cluster))*2, filename=outFile, useDingbats=FALSE)
ggsave(p, dpi=300, height=as.numeric(opt$figHeight), width=as.numeric(opt$figWidth), filename=outFile, useDingbats=FALSE)
#ggsave(p, dpi=300, height=10, width=9, filename=outFile, useDingbats=FALSE)
}
if(is.null(opt$ftrResFile)) {
outFile <- sprintf("%s/go_analysis_compareClusterFormula_%s.xls", opt$outDir, opt$annotation)
data <- as.data.frame(results@compareClusterResult)
data$geneIDf <- apply(data, 1, function(y) paste(unlist(lapply(unlist(strsplit(y[10], "/")), function(x) geneList[which(geneList$ENTREZID==x),]$V1)), collapse="/"))
write.table(data, file=outFile, sep="\t", quote=F, col.names=T, row.names=F)
if(exists("results_levels")==T) {
outFile <- sprintf("%s/go_analysis_compareClusterFormula_%s_levels.xls", opt$outDir, opt$annotation)
data_levels <- as.data.frame(results_levels@compareClusterResult)
data_levels$geneIDf <- apply(data_levels, 1, function(y) paste(unlist(lapply(unlist(strsplit(y[7], "/")), function(x) geneList[which(geneList$ENTREZID==x),]$V1)), collapse="/"))
write.table(data_levels, file=outFile, sep="\t", quote=F, col.names=T, row.names=F)
}
#opt$sessionFile <- sprintf("%s/go_analysis_compareClusterFormula_%s.Rsession", opt$outDir, opt$annotation)
rm(figWidth, figHeight, maxClass, minGene, outDir, allowDuplicates)
save.session(opt$sessionFile)
}
save.session("test.session")
} else {
## read input gene list file
gene <- read.table(opt$inFile)
opt$sessionFile <- sprintf("%s/go_analysis_list_%s.Rsession", opt$outDir, opt$annotation)
if(!file.exists(opt$sessionFile)) {
## convert gene symbol to entrez gene id
gene_conv <- bitr(gene$V1, fromType=opt$geneIdType, toType="ENTREZID", OrgDb=genomeDb)
gene_conv <- as.data.frame(gene_conv)
gene <- merge(gene, gene_conv, by.x="V1", by.y=opt$geneIdType)
gene <- gene[which(!is.na(gene$ENTREZID)),]
#row.names(gene) <- gene$entrezgene
## compute go enrichment
if(opt$annotation=="DAVID"){
## annotation options: GOTERM_BP_ALL, GOTERM_MF_ALL, GOTERM_CC_ALL, UP_TISSUE, UCSC_TFBS
results <- enrichDAVID(gene=gene$ENTREZID, idType="ENTREZ_GENE_ID", listType="Gene", annotation="GOTERM_BP_ALL", david.user="pundhir@binf.ku.dk", species=opt$genome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
} else if(opt$annotation=="KEGG_PATHWAY"){
results=enrichKEGG(gene$ENTREZID, organism=opt$genome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene))
#results=enrichKEGG(gene$ENTREZID, OrgDb=opt$genome, pvalueCutoff=as.numeric(opt$pValue), minGSSize=as.numeric(opt$minGene), use_internal_data=TRUE)
} else if(opt$annotation=="DISEASE_ONTOLOGY"){
results=enrichDO(gene$ENTREZID, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else if(opt$annotation=="REACTOME_PATHWAY"){
results=enrichPathway(gene$ENTREZID, organism=genomeReactome, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else if(opt$annotation=="GOTERM_BP_ALL"){
results=enrichGO(gene$ENTREZID, ont="BP", OrgDb=genomeDb, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else if(opt$annotation=="GOTERM_MF_ALL"){
results=enrichGO(gene$ENTREZID, ont="MF", OrgDb=genomeDb, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
} else if(opt$annotation=="GOTERM_CC_ALL"){
results=enrichGO(gene$ENTREZID, ont="CC", OrgDb=genomeDb, pvalueCutoff=as.numeric(opt$pValue), qvalueCutoff=as.numeric(opt$pValue))
}
} else {
load(opt$sessionFile)
}
save.session(opt$sessionFile)
## compute gene enrichment network for up- and down-regulated genes
if(is.numeric(gene$V2) & nrow(as.data.frame(results))>=1) {
geneList <- structure(gene$V2, names=gene$ENTREZID)
pdf(sprintf("%s/go_analysis_list_cnetplot_%s.pdf", opt$outDir, opt$annotation), width=20, height=20)
cnetplot(results, categorySize="pvalue", foldChange=geneList, showCategory = 3)
dev.off()
}
outFile <- sprintf("%s/go_analysis_list_%s.xls", opt$outDir, opt$annotation)
data <- as.data.frame(results)
if(nrow(data)>=1) {
data$geneIDf <- apply(data, 1, function(y) paste(unlist(lapply(unlist(strsplit(y[8], "/")), function(x) gene[which(gene$ENTREZID==x),]$V1)), collapse="/"))
}
write.table(data, file=outFile, sep="\t", quote=F, col.names=T, row.names=F)
#pdf(sprintf("%s/go_analysis_list_%s.pdf", opt$outDir, opt$annotation))
#dotplot(results, x="count", showCategory=opt$maxClass, colorBy="qvalue")
#barplot(results)
#dev.off()
outFile <- sprintf("%s/go_analysis_list_%s.pdf", opt$outDir, opt$annotation)
data$pvalue <- -log10(data$pvalue)
data <- data[order(-data$pvalue),]
p <- ggplot(data[c(1:opt$maxClass),], aes(x = 1, y = Description)) +
geom_point(aes(colour=pvalue,size=Count)) +
scale_colour_gradient(high="#00441b", low="#99d8c9", name="-log10(p-value)") +
scale_size_area(breaks=seq(0,100,by=10), max_size=20) +
theme(text=element_text(size=10), axis.text.x=element_text(angle=90)) +
theme_bw(base_size=15)
ggsave(p, dpi=300, height=as.numeric(opt$figHeight), width=as.numeric(opt$figWidth), filename=outFile, useDingbats=FALSE)
save.session(opt$sessionFile)
}