{"name":"clusterProfiler","category":"Enrichment","category_key":"enrichment-network","category_name":"富集与网络分析","tool_type":"R 包","summary":"R 生态常用功能富集分析工具，支持 GO、KEGG、GSEA 和多种可视化。","performance_json":{"x_axis":["200 genes","2000 genes","ranked 20k genes"],"series":[{"name":"时间消耗","unit":"分钟","y_axis":"time","data":[0.5,1.8,8.5]},{"name":"峰值内存","unit":"GB","y_axis":"memory","data":[1,2,4]}],"environment":"R 4.4 / org.Hs.eg.db / KEGG online cache"},"markdown_docs":"# clusterProfiler\n\n## 工具定位\nclusterProfiler 适合 DEG、WGCNA 模块基因、marker gene 等列表的功能解释。\n\n## 核心思想\n将基因列表映射到功能数据库，通过超几何检验或排序统计评估通路富集。\n\n## 输入与输出\n| 数据对象 | 说明 |\n| --- | --- |\n| gene list | 上游分析输入 |\n| ranked gene list | 上游分析输入 |\n| ID mapping table | 上游分析输入 |\n| GO/KEGG 富集表 | 下游解读结果 |\n| dotplot | 下游解读结果 |\n| GSEA 曲线 | 下游解读结果 |\n\n## 示例命令\n```r\nlibrary(clusterProfiler)\nlibrary(org.Hs.eg.db)\nego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID', ont = 'BP')\ndotplot(ego)\ngsea <- gseKEGG(geneList = ranked_gene_list, organism = 'hsa')\n```\n\n## 解读要点\n1. 基因 ID 类型转换是最常见错误来源。\n2. 背景基因集应与实验可检测基因范围一致。\n3. 富集结果需要合并相似 term，避免重复解释。\n","id":18,"created_at":"2026-06-03T17:48:59.833455Z"}