R获取指定GO term和KEGG pathway的gene list基因集

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")

原文地址：https://www.cnblogs.com/leezx/p/10819110.html