arxiv-database
This skill provides Python tools for searching and retrieving preprints from arXiv.org via its public Atom API. It supports keyword search,…
Maintainer FreedomIntelligence · Last updated April 1, 2026
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.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-pathway-go-enrichment
Skill Snapshot
Source Doc
Goal: Identify enriched Gene Ontology terms in a gene list from differential expression or similar analyses.
Approach: Test for over-representation of GO terms using the hypergeometric test via clusterProfiler enrichGO.
"Run GO enrichment on my gene list" → Test whether biological process, molecular function, or cellular component terms are over-represented among significant genes.
Goal: Extract significant gene IDs from differential expression results and convert to the format required by enrichGO.
Approach: Filter DE results by adjusted p-value and fold change, then convert gene symbols to Entrez IDs using bitr.
library(dplyr)
de_results <- read.csv('de_results.csv')
sig_genes <- de_results %>%
filter(padj < 0.05, abs(log2FoldChange) > 1) %>%
pull(gene_id)
## ID Conversion with bitr
**Goal:** Convert between gene identifier types (Ensembl, Symbol, Entrez) for compatibility with enrichment tools.
**Approach:** Use clusterProfiler bitr to map between ID types using organism annotation databases.
```r
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