library(KEGGREST)
library(org.Hs.eg.db)
library(tidyverse)

hsa_path_eg  <- keggLink("pathway", "hsa") %>% 
  tibble(pathway = ., eg = sub("hsa:", "", names(.)))

hsa_kegg_anno <- hsa_path_eg %>%
  mutate(
    symbol = mapIds(org.Hs.eg.db, eg, "SYMBOL", "ENTREZID"),
    ensembl = mapIds(org.Hs.eg.db, eg, "ENSEMBL", "ENTREZID")
  )

首先導(dǎo)入相應(yīng)的R包和數(shù)據(jù)。（上面程序里用到的幾個R包需要用biomanager來安裝。）hsa_kegg_anno即包含了KEGG數(shù)據(jù)庫中，所有與人有關(guān)的pathway的全部gene list。

下面我們選擇出我們想要的pathway的gene list。以hsa04066為例

result = hsa_kegg_anno[hsa_kegg_anno$pathway == 'path:hsa04066', ]

demo.png

最后將我們需要的數(shù)據(jù)保存下來

geneName_geneID = result[,3:4]
write.table(geneName_geneID,file = 'filename.txt',
            sep = '\t',row.names = FALSE,col.names = TRUE)