专治疗懒病：GO、KEGG富集分析一体函数

之前我们写过GO、KEGG的富集分析，,演示了差异基因KEGG或者GO的分析流程。其实差异基因的富集分析输入的文件只需要一组基因就可以了。所以我们发挥了专治懒病的优良传统，将KEGG、GO（BP、CC、MF）的分析封装为一个函数，您只需要提供gene，选择物种即可，只有human和mouse。而且一次性完成KEGG和GO分析结果，免去了分析两次的麻烦。这样应该也不会出错了吧。 函数内容如下：其中相关参数可按照自己的需求修改！

Enrichment_KEGGgo_analusis <- function(genes,
                                       species=c('human','mouse')){
  library(org.Hs.eg.db) 
  library(clusterProfiler)
  
  if(species == 'human'){
    
    genes_df <- bitr(genes, 
                     fromType="SYMBOL", 
                     toType="ENTREZID", 
                     OrgDb="org.Hs.eg.db", 
                     drop = TRUE) 
    
    organism = "hsa"
    OrgDb = org.Hs.eg.db
  }
  
  if(species == 'mouse'){
    
    genes_df <- bitr(genes, 
                     fromType="SYMBOL", 
                     toType="ENTREZID", 
                     OrgDb="org.Mm.eg.db", 
                     drop = TRUE) 
    organism = "mmu"
    OrgDb = org.Mm.eg.db
  }
  
  
  colnames(genes_df) <- c("gene","EntrzID")
  
  
  
  
  
  # KEGG
  kegg.re <- enrichKEGG(gene = genes_df$EntrzID, 
                        organism  = organism, 
                        keyType = "kegg",
                        pAdjustMethod = "fdr",
                        pvalueCutoff = 0.05, 
                        qvalueCutoff = 0.05, 
                        minGSSize = 10,
                        maxGSSize = 500)
  
  if (is.null(kegg.re)) {} else {kegg.re <- setReadable(kegg.re, OrgDb = OrgDb, keyType="ENTREZID")}
  print("kegg Done")
  
  # GO
  go.re1 <- enrichGO(gene = genes_df$EntrzID, 
                     keyType = "ENTREZID", 
                     OrgDb= OrgDb, 
                     ont="BP", 
                     pAdjustMethod = "fdr", 
                     pvalueCutoff  = 0.05, 
                     qvalueCutoff  = 0.05, 
                     minGSSize = 10,
                     maxGSSize = 500, 
                     readable = TRUE); 
  print("GOBP Done")
  
  go.re2 <- enrichGO(gene = genes_df$EntrzID, 
                     keyType = "ENTREZID", 
                     OrgDb= OrgDb, 
                     ont="CC", 
                     pAdjustMethod = "fdr", 
                     pvalueCutoff  = 0.05, 
                     qvalueCutoff  = 0.05, 
                     minGSSize = 10, 
                     maxGSSize = 500, 
                     readable = TRUE); 
  print("GOCC Done")
  
  go.re3 <- enrichGO(gene = genes_df$EntrzID, 
                     keyType = "ENTREZID", 
                     OrgDb= OrgDb, 
                     ont="MF", 
                     pAdjustMethod = "fdr",
                     pvalueCutoff  = 0.05, 
                     qvalueCutoff  = 0.05, 
                     minGSSize = 10, 
                     maxGSSize = 500, 
                     readable = TRUE); 
  print("GOMF Done")
  

  enrich_list <- list(kegg.re, go.re1, go.re2, go.re3)
  names(enrich_list) <- c("KEGG","GO_BP","GO_CC","GO_MF")
  return(enrich_list)
}

我们演示一下。这里我们直接用向量提供了基因。如果您的文件是差异基因，很好弄，只需要$符号传入gene symbol那一列即可。

genes <- c(c('MAST4','IL4R','SYT1','PRDM1','AUTS2','KNL1',
             'CD79A', "PLXDC2","NKG7","NELL2","BACH2","DIAPH3",
             "SYN3",  "NTNG1",  "ADAM23","SOX5","TMPO",
             "ARHGAP6","FCRL1","CD19"))
results <- Enrichment_KEGGgo_analusis(genes = genes,
                                      species = 'human')
                                      
                                      
#运行日志
载入需要的程辑包：AnnotationDbi

clusterProfiler v4.6.2  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/

If you use clusterProfiler in published research, please cite:
T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141

载入程辑包：‘clusterProfiler’

The following object is masked from ‘package:AnnotationDbi’:

    select

The following object is masked from ‘package:IRanges’:

    slice

The following object is masked from ‘package:S4Vectors’:

    rename

The following objects are masked from ‘package:plyr’:

    arrange, mutate, rename, summarise

The following object is masked from ‘package:stats’:

    filter

'select()' returned 1:1 mapping between keys and columns
Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
[1] "kegg Done"
[1] "GOBP Done"
[1] "GOCC Done"
[1] "GOMF Done"
Warning messages:
1: 程辑包‘AnnotationDbi’是用R版本4.2.2 来建造的 
2: In utils::download.file(url, quiet = TRUE, method = method, ...) :
  the 'wininet' method is deprecated for http:// and https:// URLs
3: In utils::download.file(url, quiet = TRUE, method = method, ...) :
  the 'wininet' method is deprecated for http:// and https:// URLs                         

结果分别储存在list中，这样很方便了吧！

有需要的可以试一下，总之是为了省时省力，那些在线的分析工具的底层原理也就是这样。觉得分享有用的点个赞、分享下再走呗！