AI导哈

数据与研究

pathway-enrichment

k-dense-ai/scie…t-skills

Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when 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".

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安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill pathway-enrichment

SKILL.md

Pathway Enrichment

Overview

Enrichment analysis answers "what biology is over-represented in my genes?" It is the standard last step after differential expression, a screen, or clustering. There are two core methods, and choosing correctly is the single most important decision:

  • ORA (over-representation analysis) — take a thresholded gene list (e.g., padj < 0.05) and test which gene sets it overlaps more than chance, using Fisher's exact / hypergeometric tests. Tools: Enrichr, g:Profiler.
  • GSEA (gene set enrichment analysis) — take the whole ranked list of genes (no threshold) and test whether each gene set is concentrated toward the top or bottom. Preranked GSEA uses a per-gene score (e.g., the DESeq2 stat). Better when effects are broad and subtle.

This skill orchestrates these analyses, the gene-set databases behind them, and the interpretation pitfalls that make results wrong or unpublishable.

When to Use This Skill

Use this skill when the user wants to:

  • Find enriched GO terms / KEGG / Reactome / WikiPathways / MSigDB Hallmark sets in a gene list.
  • Run GSEA / preranked GSEA on DESeq2, edgeR, limma, or Scanpy rank_genes_groups output.
  • Score gene-set expression per sample/cell (ssGSEA, GSVA).
  • Interpret, deduplicate, and visualize enrichment results, or build a publication table/figure.
  • Decide between ORA and GSEA, pick gene-set libraries, choose a background, or fix gene-ID problems.

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