Data & ReproBioinformatics & GenomicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-pathway-gsea

Maintainer FreedomIntelligence · Last updated April 1, 2026

Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.

OpenClawNanoClawAnalysisReproductionbio-pathway-gsea🧬 bioinformatics (gptomics bio-* suite)bioinformatics — pathway & network analysisgene

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-pathway-gsea

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

gene_list <- de_results$log2FoldChange names(gene_list) <- de_results$gene_id.
gene_list <- sort(gene_list, decreasing = TRUE).

Source Doc

Excerpt From SKILL.md

Core Concept

GSEA uses all genes ranked by a statistic (log2FC, signed p-value) rather than a subset of significant genes. It finds gene sets where members are enriched at the top or bottom of the ranked list.

Prepare Ranked Gene List

Goal: Create a sorted named vector of gene-level statistics suitable for GSEA input.

Approach: Extract fold changes (or other statistics) from DE results, name by gene ID, and sort in decreasing order.

"Run GSEA on my differential expression results" → Rank all genes by expression statistic and test whether predefined gene sets cluster toward the extremes of the ranked list.

library(clusterProfiler)
library(org.Hs.eg.db)

de_results <- read.csv('de_results.csv')

## Convert Gene IDs for GSEA

**Goal:** Map gene symbols to Entrez IDs while preserving the ranked statistic values.

**Approach:** Use bitr for ID conversion, then rebuild the named sorted vector with Entrez IDs as names.

```r