数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-pathway-gsea

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-pathway-gsea🧬 bioinformatics (gptomics bio-* suite)bioinformatics — pathway & network analysisgene

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

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

原始文档

SKILL.md 摘录

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