bio-pathway-go-enrichment
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
npx skills add BioTender-max/awesome-bio-agent-skills --skill go-enrichment --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.
## Version Compatibility Reference examples tested with: R stats (base), clusterProfiler 4.10+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # GO Over-Representation Analysis ## When to Use ORA vs GSEA | Scenario | Method | Why | |----------|--------|-----| | Clear DE gene list with arbitrary cutoff (padj + FC) | ORA, but consider GSEA instead | ORA discards magnitude; GSEA uses all genes ranked by statistic | | Genes from co-expression module, GWAS loci, screen hits | ORA | No ranking available; ORA is appropriate | | All genes with DE statistics available | GSEA (gseGO) | Avoids arbitrary cutoff; detects subtle coordinated changes | | Very few DE genes (< 20) | GSEA | ORA has no power with small lists | | RNA-seq with known length bias | GOseq (goseq package) | Standard ORA ignores length bias; longer genes are more likely DE | ORA converts continuous measures into binary (significant/not), losing information. When i
- Version Compatibility
- When to Use ORA vs GSEA
- Core Pattern
- Prepare Gene List from DE Results
- ID Conversion with bitr
- Background Universe (Critical)
- All Three Ontologies
- Make Results Readable
- Extract and Export Results
- Simplify Redundant Terms
- Different Organisms
- Group GO Terms by Ancestor
- Key Parameters
- Interpreting Results
What does the bio-pathway-go-enrichment skill do?
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill go-enrichment --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.