ClusterProfiler做代谢物富集分析

使用 clusterProfiler 的内置富集函数做代谢物富集分析。

# 加载必要的包
library(clusterProfiler)

## 

## Registered S3 methods overwritten by 'treeio':
##   method              from    
##   MRCA.phylo          tidytree
##   MRCA.treedata       tidytree
##   Nnode.treedata      tidytree
##   Ntip.treedata       tidytree
##   ancestor.phylo      tidytree
##   ancestor.treedata   tidytree
##   child.phylo         tidytree
##   child.treedata      tidytree
##   full_join.phylo     tidytree
##   full_join.treedata  tidytree
##   groupClade.phylo    tidytree
##   groupClade.treedata tidytree
##   groupOTU.phylo      tidytree
##   groupOTU.treedata   tidytree
##   inner_join.phylo    tidytree
##   inner_join.treedata tidytree
##   is.rooted.treedata  tidytree
##   nodeid.phylo        tidytree
##   nodeid.treedata     tidytree
##   nodelab.phylo       tidytree
##   nodelab.treedata    tidytree
##   offspring.phylo     tidytree
##   offspring.treedata  tidytree
##   parent.phylo        tidytree
##   parent.treedata     tidytree
##   root.treedata       tidytree
##   rootnode.phylo      tidytree
##   sibling.phylo       tidytree

## clusterProfiler v4.10.0  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

## 
## Attaching package: 'clusterProfiler'

## The following object is masked from 'package:stats':
## 
##     filter

# 构造感兴趣的代谢物列表
metabolite_list = c("Metabolite1", "Metabolite2", "Metabolite3", "Metabolite6", "Metabolite7")

# 构造代谢途径与代谢物的映射关系 (TERM2GENE)
term2gene = data.frame(
    Pathway = c("Pathway1", "Pathway1", "Pathway1", "Pathway2", "Pathway2", 
                "Pathway3", "Pathway3", "Pathway4", "Pathway4", "Pathway5"),
    Metabolite = c("Metabolite1", "Metabolite2", "Metabolite3", "Metabolite4", "Metabolite5", 
                   "Metabolite6", "Metabolite7", "Metabolite8", "Metabolite9", "Metabolite10")
)

# 构造代谢途径描述信息 (TERM2NAME)
term2name = data.frame(
    Pathway = c("Pathway1", "Pathway2", "Pathway3", "Pathway4", "Pathway5"),
    Description = c("Metabolic Pathway 1", "Metabolic Pathway 2", "Metabolic Pathway 3", 
                    "Metabolic Pathway 4", "Metabolic Pathway 5")
)

# 使用 enricher 函数进行代谢物富集分析
result = enricher(
    gene = metabolite_list,
    pvalueCutoff = 0.5, # 调高 cutoff 以得到结果
    pAdjustMethod = "BH",
    TERM2GENE = term2gene,
    TERM2NAME = term2name,
    minGSSize = 1  # 将默认的 10 改成 1，否则也得不到结果
)

# 查看结果
if (!is.null(result)) {
    print(head(as.data.frame(result)))
    # 可视化结果
    dotplot(result, showCategory = 5)
} else {
    message("没有显著富集的代谢途径")
}

##                ID         Description GeneRatio BgRatio     pvalue  p.adjust
## Pathway1 Pathway1 Metabolic Pathway 1       3/5    3/10 0.08333333 0.1666667
##             qvalue                              geneID Count
## Pathway1 0.1666667 Metabolite1/Metabolite2/Metabolite3     3