kegg-pathway-analysis
Guide to KEGG pathway enrichment for DEG results. Covers ORA vs GSEA, mandatory directionality splitting, KEGG organism codes, API failure handling with offline fallbacks, cross-condition comparisons, and answer-first reporting. Consult when running enrichment with clusterProfiler or gseapy.
npx skills add BioTender-max/awesome-bio-agent-skills --skill kegg-pathway-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# KEGG Pathway Enrichment Analysis Guide ## Overview KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis identifies biological pathways that are statistically over-represented among differentially expressed genes. This guide covers the two main enrichment approaches (ORA and GSEA), critical workflow decisions such as splitting genes by directionality, tool selection between R clusterProfiler and Python gseapy, and strategies for handling the notoriously unreliable KEGG REST API. It addresses recurring failure modes that produce incorrect pathway counts or stalled analyses. The three most common errors in KEGG pathway analysis are: (1) combining up-regulated and down-regulated genes into a single enrichment run, which masks true pathway signals; (2) analysis failures caused by KEGG REST API timeouts with no fallback strategy; and (3) delaying result reporting while attempting cosmetic pathway name lookups that may never complete. This guide provides concrete solutions for each. ## Key Concepts ### ORA vs GSEA Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) are the two primary methods for pathway enrichment, and they differ in both in
- Overview
- Key Concepts
- ORA vs GSEA
- Directionality in Enrichment
- KEGG Organism Codes
- KEGG API Reliability
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
What does the kegg-pathway-analysis skill do?
Guide to KEGG pathway enrichment for DEG results. Covers ORA vs GSEA, mandatory directionality splitting, KEGG organism codes, API failure handling with offline fallbacks, cross-condition comparisons, and answer-first reporting. Consult when running enrichment with clusterProfiler or gseapy.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill kegg-pathway-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.