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pathway-enrichment

Analyze gene lists or ranked data for enriched biological pathways and gene sets.

13|3|Updated Jun 10, 2026
Search Tags:#pathway analysis#GSEA#enrichment analysis#ORA#gene set
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill pathway-enrichment-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pathway-enrichment
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/pathway-enrichment
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill pathway-enrichment-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gseapy, gprofiler-official, pandas, numpy, scipy, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of interpreting gene lists and ranked gene data by providing a comprehensive suite of tools for pathway and gene-set enrichment analysis.

Core Features & Use Cases

  • Pathway and Gene-Set Enrichment: Analyze gene lists or ranked gene data to identify over-represented biological pathways, GO terms, or gene sets.
  • Over-representation Analysis (ORA): Test which gene sets are over-represented using Fisher's exact test or hypergeometric test.
  • Gene Set Enrichment Analysis (GSEA): Analyze the concentration of gene sets toward the top or bottom of a ranked list.
  • Single-sample Scoring: Score pathway activity per sample/cell using ssGSEA or GSVA.
  • Use Case: For researchers with differentially expressed genes from PyDESeq2/Scanpy or CRISPR-screen hits, this Skill helps to identify the biological pathways or gene sets that are over-represented.

Quick Start

Run the pathway-enrichment skill to analyze the gene list 'differentially_expressed_genes.txt' using the default gene-set libraries.

Frequently Asked Questions about pathway-enrichment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform gene set enrichment analysis on a ranked gene list?▼

Gene set enrichment analysis (GSEA) evaluates whether gene sets are concentrated at the top or bottom of a ranked list. This Skill applies GSEA to ranked gene data to identify statistically over-represented biological pathways using the gseapy library.

What is the difference between ORA and GSEA for pathway analysis?▼

Over-representation analysis (ORA) uses Fisher's exact or hypergeometric tests on discrete gene lists, while GSEA analyzes the concentration of gene sets across a ranked list. This Skill supports both methods to identify over-represented biological pathways.

Can I score pathway activity per sample using ssGSEA or GSVA?▼

Yes, single-sample scoring calculates pathway activity per sample or cell using ssGSEA or GSVA methods. This capability allows researchers to evaluate pathway enrichment levels across individual samples rather than broad gene lists.

Do I need network access to run over-representation analysis on differentially expressed genes?▼

Yes, network access for external databases is required to conduct over-representation analysis. The Skill queries external biological pathway and gene-set libraries to identify enriched GO terms in differentially expressed genes.

What's the best way to identify enriched biological pathways from CRISPR screen hits?▼

Analyzing CRISPR-screen hits with over-representation analysis identifies over-represented biological pathways and gene sets. This Skill processes discrete gene lists to detect statistically significant pathway enrichments using Fisher's exact test.

How do I visualize results from pathway and gene-set enrichment analysis?▼

Visualizing pathway and gene-set enrichment analysis results requires matplotlib to generate graphical outputs. The Skill leverages matplotlib alongside pandas and numpy to plot and interpret the enriched biological pathways identified.

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