Individual skill
tooluniverse-gene-enrichment
Gene-set enrichment analysis — GO (Biological Process, Molecular Function, Cellular Component), KEGG, Reactome pathway enrichment via clusterProfiler, gseapy, ORA, GSEA, plus g:Profiler for enrichment/ID conversion/ortholog mapping/SNP-to-gene annotation. Use for interpreting DEG lists, screen hit lists, or any gene-list-to-pathways query. Includes simplify-cutoff handling and union-vs-total denominator conventions for percent-DE questions.
skills/tooluniverse-gene-enrichment/SKILL.md
Source commit 5598a9fc97e32964fe9b703818e7d0747142f179
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- agents Repository topic
- reasoning-agent Repository topic
- reasoning-language-models Repository topic
- tool-use Repository topic
- ai-agents Repository topic
- ai-scientists Repository topic
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.env.templateasset · 499 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesSKILL.mdcore · 32,218 bytesreferences/enrichr_guide.mddoc · 10,519 bytesreferences/gsea_workflow.mddoc · 12,633 bytesreferences/ora_workflow.mddoc · 11,934 bytesreferences/tool_parameters.mddoc · 11,778 bytesreferences/troubleshooting.mddoc · 14,417 bytesscripts/condition_enrichment_screen.pyruns · 12,245 bytesscripts/enrichgo_runner.pyruns · 10,332 bytesscripts/format_enrichment_output.pyruns · 14,945 bytesscripts/gseapy_enrichment_runner.pyruns · 15,892 bytestest_gene_enrichment.pyruns · 51,251 bytes
Read excerpt
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Gene Enrichment and Pathway Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed enrichment files (CSV/TSV named `*enrich*`, `*go*`, `*kegg*`, `*reactome*`, `*ego*`, `*_simplified.csv`) → read directly
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running enrichment from raw DEG lists produces different numbers than the published answer due to subtle filter differences upstream, and is much slower.
