{ "ok": true, "resource": { "id": "RES_9DA179F3AF95", "resource_type": "skill", "canonical_url": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/pathway-enrichment", "name": "pathway-enrichment", "summary_en": "Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for \"pathway analysis\", \"enrichment analysis\", \"GO enrichment\", \"KEGG/Reactome pathways\", \"GSEA\", \"over-representation\", \"functional annotation\", or \"what pathways are my genes in\".", "summary_zh": "对基因列表或排序基因数据进行通路和基因集富集分析，然后解读结果。当用户拥有一组基因（来自PyDESeq2/Scanpy的差异表达基因、CRISPR筛选命中、聚类标记基因、蛋白质组学命中）并想知道哪些生物学通路、GO术语或基因集被过度代表或富集时使用。涵盖过度代表分析（ORA / Enrichr / Fisher / 超几何）、排序基因集富集分析（GSEA / 预排序）、单样本评分（ssGSEA/GSVA），以及通过gseapy、g:Profiler、Enrichr库、MSigDB、GO、KEGG、Reactome和WikiPathways进行功能分析——还包括基因ID映射、选择正确的背景宇宙、多重检验校正、冗余减少、点图/富集图以及可发表的表格。用于“通路分析”、“富集分析”、“GO富集”、“KEGG/Reactome通路”、“GSEA”、“过度代表”、“功能注释”或“我的基因在哪些通路中”。", "description_en": "This skill performs pathway and gene-set enrichment analysis on gene lists or ranked gene data, covering over-representation analysis (ORA), ranked Gene Set Enrichment Analysis (GSEA), and single-sample scoring (ssGSEA/GSVA). 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