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gene_enrichment.r
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#!/usr/bin/env Rscript
#########################################################
# Gene Enrichment analysis module using clusterProfiler #
# database: KEGG, msigdb, GO, ENCODE ChIP-Seq #
#########################################################
args=commandArgs(TRUE)
library("getopt")
spec <- matrix(c(
'query_gene' , 'g', 1, "character","gene",
'cutoff_pos' , 'p', 1, "numeric"," positive correlation cutoff, -p 0.5 ",
'cutoff_neg' , 'n', 1, "numeric"," inverse correlation cutoff, -n -0.5 ",
'cutoff_reg' , 'r', 1, "character"," interval for excleude(ex) or include(in), -r ex, -c in ",
'enrich_type' , 'e', 1, "character"," enrichment geneset , -e bp ,GO BP. -e cc ,GO cc. -e mf ,GO mf. -e kegg ,kegg. -e hm, msigdb hallmark. -e tf, transcription factor. -e encodetf, ENCODE transcription factor "
),ncol=5,byrow=T)
opt = getopt(spec);
if ( is.null(opt$enrich_type) |is.null(opt$query_gene )) {
cat(paste(getopt(spec, usage=T),"\nExample (linux): ./gene_enrichment.r -p 0.5 -n -0.5 -r ex -g chrX_47755339_47705503_fwd -e bp \n"));
q();
}
#https://www.r-bloggers.com/kegg-enrichment-analysis-with-latest-online-data-using-clusterprofiler/
#library(goseq)
library("clusterProfiler")
library(org.Hs.eg.db)
library(ggplot2)
library(visNetwork)
source("universal_function.R")
query_gene<-opt$query_gene[1]
cutoff_pos<-opt$cutoff_pos[1]
cutoff_neg<-opt$cutoff_neg[1]
cutoff_reg<-opt$cutoff_reg[1]
enrich_type<-opt$enrich_type[1]
library(feather)
run_time_message("loading linc_coexp_pairs")
linc_coexp_pairs<-as.data.frame(read_feather(paste0("output/linc_coexp_pairs.feather")))
df_opt<-readRDS(paste0("output/opt.rds"))
if ("gencodev19" %in% df_opt["annotation",1] ){
gene_coordinate<-readRDS("v19_gene_coordinate.rds")
} else if ("gencodev25" %in% df_opt["annotation",1] ) {
gene_coordinate<-readRDS("v25_gene_coordinate.rds")
}
# loading database files
db_path<-list(gencodev19=data.frame(msigdb_hm="msigdb/h.all.v5.2.entrez.gmt",
msigdb_tf="msigdb/c3.tft.v5.2.entrez.gmt",
encode_tf="enchriment_ENCODE_hg19_ChIP_3000_1000_q0.01.feather"),
gencodev25=data.frame(msigdb_hm="msigdb/h.all.v5.2.entrez.gmt",
msigdb_tf="msigdb/c3.tft.v5.2.entrez.gmt",
encode_tf="enchriment_ENCODE_hg38_ChIP_3000_1000_q0.01.feather")
)
msigdb_hm<-as.character(db_path[[df_opt["annotation",1]]][1,"msigdb_hm"])
msigdb_tf<-as.character(db_path[[df_opt["annotation",1]]][1,"msigdb_tf"])
encode_tf<-as.character(db_path[[df_opt["annotation",1]]][1,"encode_tf"])
run_time_message(paste0("Analysis started\n",
"Query: ",query_gene,"\n",
"cutoff pos: ",cutoff_pos,"\n",
"cutoff neg: ",cutoff_neg,"\n",
"cutoff reg: ",cutoff_reg,"\n",
"enrich_type: ",enrich_type,"\n"
))
# co-expressed genes filter
################################################################################
rna_idx<-ifelse( length(which("lncRNA" %in% colnames(linc_coexp_pairs))) == 1,which("lncRNA" %in% colnames(linc_coexp_pairs)),
ifelse(length(which("circRNA" %in% colnames(linc_coexp_pairs))) == 1,which("circRNA" %in% colnames(linc_coexp_pairs)),"NA"))
if (rna_idx =="NA"){q();}
rnacolumn<-linc_coexp_pairs[,colnames(linc_coexp_pairs)[rna_idx]]
linc_coexp_pairs_filtered<-linc_coexp_pairs[ rnacolumn %in% query_gene , ]
linc_coexp_pairs_filtered<-linc_coexp_pairs_filtered[!is.na(linc_coexp_pairs_filtered$cor),]
if( cutoff_reg == "ex") {
linc_coexp_pairs_filtered<-linc_coexp_pairs_filtered[which(linc_coexp_pairs_filtered$cor > cutoff_pos | linc_coexp_pairs_filtered$cor < cutoff_neg), ]
}
if(cutoff_reg == "in" ) {
linc_coexp_pairs_filtered<-linc_coexp_pairs_filtered[which(!(linc_coexp_pairs_filtered$cor > cutoff_pos & linc_coexp_pairs_filtered$cor < cutoff_neg)), ]
}
##################################################################################
write_error_fun<-function(comment){
run_time_message(paste0("Error: ",comment))
# error tab
write.table(data.frame(Error=paste0(comment)),
paste0("output/enrichment_res_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,"_.txt")
,sep = '\t',row.names = F,quote = F)
# bar plot
error_df<-data.frame(Description=paste0(comment),pvalue=0.05)
error_df$Description<-gsub(",",",\n",error_df$Description)
width=4.5
height=2
ggplot(error_df, aes(x=Description, y= -log10(pvalue)) )+
geom_bar(stat="identity") +
theme_bw(base_size = 11) +
coord_flip()+
xlab("")+
ylab("pvalue (-log10)")+
#coord_fixed(ra1)+
scale_x_discrete(limits=paste(error_df$Description[length(error_df$Description):1]))+ # order by original df
theme(
aspect.ratio = 2,
panel.grid.minor = element_blank(),
plot.title = element_text(hjust = 1)
) +
scale_y_continuous( expand = c(0.02,0.02) )+
ggtitle("No enrichment result available")
ggsave(paste0("output/enrichment_res_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,"_.png"),height = height,width =width,dpi=300 )
# error node and link
node_df<-data.frame(label=c("No network result available",paste0(comment)))
node_df<-cbind(data.frame(id=rep(1:nrow(node_df)),stringsAsFactors=F),node_df) # generate ID
link_df<-data.frame(from=1,to=2)
write.table(link_df,paste0("output/pathway_network_link_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,".txt"),sep = "\t",quote=F,row.names = F)
write.table(node_df,paste0("output/pathway_network_node_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,".txt"),sep = "\t",quote=F,row.names = F)
}
gene_symbol<-linc_coexp_pairs_filtered$co_exp_gene
run_time_message(paste0("number of gene before entrenz id conversion: ", length(gene_symbol[!gene_symbol %in% query_gene])))
if(length(gene_symbol[!gene_symbol %in% query_gene])<=0) {write_error_fun("No co-expressed gene, please lower the correlation cutoff");q() }
gene_coordinate_noNA<-gene_coordinate[!is.na(gene_coordinate$entrezgene),]
# entrenz ID conversion
gene.df1 <- try(bitr(unique(linc_coexp_pairs_filtered$co_exp_gene), fromType = "SYMBOL", toType = c("ENTREZID", "ENSEMBL"),OrgDb = "org.Hs.eg.db"))
gene.df3 <- try(bitr(unique(linc_coexp_pairs$co_exp_gene), fromType="SYMBOL", toType= c("ENTREZID", "ENSEMBL"), OrgDb="org.Hs.eg.db"))
if(class(gene.df1)=="try-error") { write_error_fun("entrenz ID conversion error, please contact authors");q() }
gene<-gene.df1$ENTREZID
# prep background genes
gene_bd<-unique(Reduce(union, list(gene.df3$ENTREZID)))
gene_bd_symbol<-linc_coexp_pairs$co_exp_gene
run_time_message(paste0("number of gene after entrenz id conversion: ", length(gene)))
if(length(gene)<=0) {write_error_fun("No co-expressed gene after entrenz ID conversion, please lower the correlation cutoff");q() }
# kegg function
run_kegg<-function(){
enrich_res <- enrichKEGG(gene, organism="hsa",
pvalueCutoff = 0.05,
keyType = "kegg",
minGSSize=5,
maxGSSize=2000,
pAdjustMethod="BH",
universe=gene_bd)
enrich_res<-as.data.frame(enrich_res)
return(enrich_res)
}
# go mf function
run_mf<-function(){
enrich_res<-enrichGO(gene,
'org.Hs.eg.db',
pvalueCutoff = 0.05,
ont = "MF",
pAdjustMethod = "BH",
minGSSize=5,
maxGSSize=2000,
universe=gene_bd )
enrich_res<-as.data.frame(enrich_res)
return(enrich_res)
}
# go bp function
run_bp<-function(){
enrich_res<-enrichGO(gene,
'org.Hs.eg.db',
ont = "BP",
minGSSize=5,
maxGSSize=2000,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe=gene_bd )
enrich_res<-as.data.frame(enrich_res)
return(enrich_res)
}
# go cc function
run_cc<-function(){
enrich_res<-enrichGO(gene,
'org.Hs.eg.db',
ont = "CC",
minGSSize=5,
maxGSSize=2000,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe=gene_bd )
#head(summary(goCC))
enrich_res<-as.data.frame(enrich_res)
return(enrich_res)
}
# msigdb function
run_hm<-function(){
h <- read.gmt(msigdb_hm)
enrich_res<- enricher(gene, TERM2GENE=h,
universe=gene_bd,
pvalueCutoff = 0.05,
minGSSize=5,
maxGSSize=2000,
pAdjustMethod = "BH")
enrich_res<-as.data.frame(enrich_res)
enrich_res$Description<-gsub("HALLMARK_","",enrich_res$Description)
enrich_res$Description<-gsub("_"," ",enrich_res$Description)
return(enrich_res)
}
# transcription factor motif function
run_tf<-function(){
h <- read.gmt(msigdb_tf)
enrich_res<- enricher(gene, TERM2GENE=h,
universe=gene_bd,
minGSSize=5,
maxGSSize=2000,
pvalueCutoff = 0.05,
pAdjustMethod = "BH")
enrich_res<-as.data.frame(enrich_res)
return(enrich_res)
}
# encode ChIP-Seq function
run_encodetf<-function(){
library(feather)
encode_chipp<-as.data.frame(read_feather(encode_tf))
encode_chipp<-encode_chipp[,c("ont","gene")]
enrich_res<- enricher(gene_symbol, TERM2GENE=encode_chipp,
universe=gene_bd_symbol,
minGSSize=5,
maxGSSize=40000,
pvalueCutoff = 0.05,
pAdjustMethod = "BH")
enrich_res<-as.data.frame(enrich_res)
return(enrich_res)
}
if (enrich_type == "kegg") {
enrich_res <- try(run_kegg())
}
if (enrich_type == "cc") {
enrich_res <- try(run_cc())
}
if (enrich_type == "mf") {
enrich_res <- try(run_mf())
}
if (enrich_type == "bp") {
enrich_res <- try(run_bp())
}
if (enrich_type == "hm") {
enrich_res <- try(run_hm())
}
if (enrich_type == "tf") {
enrich_res <- try(run_tf())
}
if (enrich_type == "encodetf") {
enrich_res <- try(run_encodetf())
}
if(class(enrich_res)=="try-error") { write_error_fun(paste0("Enrichment error, number of input gene after entrenz ID conversion: ",length(gene)));q() }
egmt_df<-as.data.frame(enrich_res)
egmt_df<-egmt_df[!is.na(egmt_df$pvalue),]
run_time_message(paste0("number of enriched terms: ", nrow(egmt_df)))
if(is.null(nrow(egmt_df)) ) { write_error_fun(paste0("Enrichment error, number of input gene after entrenz ID conversion: ",length(gene)));q() }
if(nrow(egmt_df)<=0) { write_error_fun(paste0("No term was enriched, number of input gene after entrenz ID conversion: ",length(gene)));q() }
# entrenz to symbol
if (enrich_type!= "encodetf"){
term_id_dup<-data.frame()
for (j in seq(1:nrow(egmt_df[,]))){
term_id_dup_tmp<-egmt_df[rep(j, each=length(strsplit(egmt_df[j,]$geneID,"/")[[1]])),]
term_id_dup_tmp$geneID<-strsplit(as.character(egmt_df[j,]$geneID),"/")[[1]]
symbol_entrenz<-gene.df1[gene.df1$ENTREZID %in% unique(term_id_dup_tmp$geneID),]
term_id_dup_tmp<-merge(term_id_dup_tmp,symbol_entrenz,by.x= "geneID", by.y= "ENTREZID",all.x=T)
egmt_df[j,]$geneID<-paste(unique(term_id_dup_tmp$SYMBOL),collapse = "/", sep = "")
term_id_dup<-rbind(term_id_dup,term_id_dup_tmp)
}
}
egmt_df<-data.frame(egmt_df[order(egmt_df$pvalue,decreasing = F),])
egmt_df<-egmt_df[,c(2,5,6,7,8,9)]
colnames(egmt_df)[5]<-"gene"
write.table(egmt_df,
paste0("output/enrichment_res_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,"_.txt"),sep = '\t',row.names = F,quote = F)
run_time_message("writing enrichment table")
# remove pvalue=0
egmt_df<-egmt_df[egmt_df$pvalue !=0,]
# output most signifcant terms
if(nrow(egmt_df)> 15 ){
egmt_df_15<-egmt_df[1:15,]
} else {
egmt_df_15<-egmt_df
}
if(nrow(egmt_df)<=0) { write_error_fun(paste0("No term was enriched, number of input gene after entrenz ID conversion: ",length(gene)));q() }
# break the terms that have too long length
# for (stt in seq(1,nrow(egmt_df_15))) {
# x<-gregexpr(" ",egmt_df_15$Description[stt])[[1]][7]
# if (!is.na(x)) {substring(egmt_df_15$Description[stt], x, x) <-"\n"
# } else {next}
# }
width=(max(nchar(egmt_df_15$Description))/16)+4
height=(nrow(egmt_df_15)/10)+3
ggplot(egmt_df_15, aes(x=Description, y= -log10(pvalue)) )+
geom_bar(stat="identity") +
theme_bw(base_size = 11) +
coord_flip()+
xlab("")+
ylab("pvalue (-log10)")+
#coord_fixed(ra1)+
scale_x_discrete(limits=paste(egmt_df_15$Description[length(egmt_df_15$Description):1]))+ # order by original df
theme(
#axis.text.x = element_text(size = 18),
#axis.text.y = element_text(size =13),
#legend.key = element_rect(fill = "navy"),
#plot.title = element_text(size=18, hjust =0),
aspect.ratio = 2,
#legend.background = element_rect(fill = "white"),
panel.grid.minor = element_blank()
#axis.text.x = element_text(angle = 0, hjust = 1, vjust =1),
#aspect.ratio = 2,
#panel.grid.major.x = element_blank()+
#plot.margin = unit( c(0,0,0,0) , "in" )
) +
scale_y_continuous( expand = c(0.02,0.02) )
ggsave(paste0("output/enrichment_res_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,"_.png"),height = height,width =width,dpi=300 )
run_time_message("output barplot, finished\n")
# plot network
#prep node
maxrow<-ifelse(nrow(egmt_df) >=3,3,nrow(egmt_df))
egmt_df_maxrow<-egmt_df[ 1:maxrow ,]
node_df<-data.frame(label=unique(c(egmt_df_maxrow$Description,unique(unlist(strsplit(egmt_df_maxrow$gene,"/"))))))
node_df$group<-ifelse( node_df$label %in% egmt_df_maxrow$Description,"terms","genes")
node_df<-cbind(data.frame(id=rep(1:nrow(node_df)),stringsAsFactors=F),node_df) # generate ID
node_df$value<-""
node_df[node_df$group %in% "terms",]$value<-5
node_df[node_df$group %in% "genes",]$value<-2
node_df$title<-paste0(node_df$label) #add tooltip
#prep link
link_df<-data.table::as.data.table(egmt_df_maxrow ,key="Description")
link_df<- link_df[, list(gene = unlist(strsplit(gene, "/"))), by=Description]
names(link_df)<-paste0("link_",names(link_df))
#merge link table
link_df<-merge(link_df,node_df[node_df$group %in% "terms",1:2] ,by.x="link_Description" , by.y="label")
colnames(link_df)[grep("^id$",colnames(link_df))]<-"from" # from_id == link_Description
#merge co_express_gene_id to link table
link_df<-merge(link_df,node_df[!node_df$group %in% "gene",1:2],by.x="link_gene", by.y="label" )
colnames(link_df)[grep("^id$",colnames(link_df))]<-"to"
write.table(link_df,paste0("output/pathway_network_link_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,".txt"),sep = "\t",quote=F,row.names = F)
write.table(node_df,paste0("output/pathway_network_node_",enrich_type,"_",query_gene,"_",cutoff_reg,"_",cutoff_pos,cutoff_neg,".txt"),sep = "\t",quote=F,row.names = F)