clusterProfiler没有显性的接口，但是可以直接扣取clusterProfiler里的函数。
核心函数就是get_GO_data
GO_DATA <- get_GO_data("org.Hs.eg.db", "BP", "SYMBOL") 可以看到输入的是GO数据库，选定类别，基因名字类型，输出的就是整个数据库。
但是想调用这个函数没那么简单，得导入一系列的基础函数。
一个常见的任务就是获取GO数据库里所有的cell cycle相关的基因，这需要从我们的基因集里移除。
有了这个函数，我们就可以这么做了，两句R代码搞定。
cellCycleGO <- names(GO_DATA$PATHID2NAME[grep("cell cycle|DNA replication|cell division|segregation", GO_DATA$PATHID2NAME)])
cellCycleGene <- unique(unlist(GO_DATA$PATHID2EXTID[cellCycleGO]))
print(length(cellCycleGene))
library(DOSE)
library(GOSemSim)
library(clusterProfiler)
library(org.Hs.eg.db)
#
get_GO_data <- function(OrgDb, ont, keytype) {
GO_Env <- get_GO_Env()
use_cached <- FALSE if (exists("organism", envir=GO_Env, inherits=FALSE) &&
exists("keytype", envir=GO_Env, inherits=FALSE)) { org <- get("organism", envir=GO_Env)
kt <- get("keytype", envir=GO_Env) if (org == DOSE:::get_organism(OrgDb) &&
keytype == kt &&
exists("goAnno", envir=GO_Env, inherits=FALSE)) {
## https://github.com/GuangchuangYu/clusterProfiler/issues/182
## && exists("GO2TERM", envir=GO_Env, inherits=FALSE)){ use_cached <- TRUE
}
} if (use_cached) {
goAnno <- get("goAnno", envir=GO_Env)
} else {
OrgDb <- GOSemSim:::load_OrgDb(OrgDb)
kt <- keytypes(OrgDb)
if (! keytype %in% kt) {
stop("keytype is not supported...")
} kk <- keys(OrgDb, keytype=keytype)
goAnno <- suppressMessages(
select(OrgDb, keys=kk, keytype=keytype,
columns=c("GOALL", "ONTOLOGYALL"))) goAnno <- unique(goAnno[!is.na(goAnno$GOALL), ]) assign("goAnno", goAnno, envir=GO_Env)
assign("keytype", keytype, envir=GO_Env)
assign("organism", DOSE:::get_organism(OrgDb), envir=GO_Env)
} if (ont == "ALL") {
GO2GENE <- unique(goAnno[, c(2,1)])
} else {
GO2GENE <- unique(goAnno[goAnno$ONTOLOGYALL == ont, c(2,1)])
} GO_DATA <- DOSE:::build_Anno(GO2GENE, get_GO2TERM_table()) goOnt.df <- goAnno[, c("GOALL", "ONTOLOGYALL")] %>% unique
goOnt <- goOnt.df[,2]
names(goOnt) <- goOnt.df[,1]
assign("GO2ONT", goOnt, envir=GO_DATA)
return(GO_DATA)
} get_GO_Env <- function () {
if (!exists(".GO_clusterProfiler_Env", envir = .GlobalEnv)) {
pos <- 1
envir <- as.environment(pos)
assign(".GO_clusterProfiler_Env", new.env(), envir=envir)
}
get(".GO_clusterProfiler_Env", envir = .GlobalEnv)
} get_GO2TERM_table <- function() {
GOTERM.df <- get_GOTERM()
GOTERM.df[, c("go_id", "Term")] %>% unique
} get_GOTERM <- function() {
pos <- 1
envir <- as.environment(pos)
if (!exists(".GOTERM_Env", envir=envir)) {
assign(".GOTERM_Env", new.env(), envir)
}
GOTERM_Env <- get(".GOTERM_Env", envir = envir)
if (exists("GOTERM.df", envir = GOTERM_Env)) {
GOTERM.df <- get("GOTERM.df", envir=GOTERM_Env)
} else {
GOTERM.df <- toTable(GOTERM)
assign("GOTERM.df", GOTERM.df, envir = GOTERM_Env)
}
return(GOTERM.df)
}
获取KEGG的通路和基因是一样的，也是用clusterProfiler
代码：
hsa_kegg <- clusterProfiler::download_KEGG("hsa")
names(hsa_kegg)
head(hsa_kegg$KEGGPATHID2NAME)
head(hsa_kegg$KEGGPATHID2EXTID)
PATH2ID <- hsa_kegg$KEGGPATHID2EXTID
PATH2NAME <- hsa_kegg$KEGGPATHID2NAME
PATH_ID_NAME <- merge(PATH2ID, PATH2NAME, by="from")
colnames(PATH_ID_NAME) <- c("KEGGID", "ENTREZID", "DESCRPTION")
# write.table(PATH_ID_NAME, "HSA_KEGG.txt", sep="\t")
library(biomaRt)
mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
entrezgene <- PATH_ID_NAME$ENTREZID
# This step need some time
ensembl_gene_id<- getBM(attributes=c("ensembl_gene_id", "entrezgene"),
filters = "entrezgene",
values=entrezgene , mart= mart)
PATH_ID_NAME <- merge(PATH_ID_NAME, ensembl_gene_id, by.x= "ENTREZID",by.y= "entrezgene")