/pathway-enrichment

name: pathway-enrichment
description: Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".
license: MIT
compatibility: Requires Python 3.12+ with gseapy 1.3.1, numpy, pandas and matplotlib. Optional gprofiler-official, mygene and lxml for online mapping/catalogs. Online queries need network access; local GMT workflows run offline.
metadata:
  version: "1.3"
  last-reviewed: "2026-10-01"
  skill-author: K-Dense Inc.

Pathway Enrichment

Overview

Enrichment analysis answers "what biology is over-represented in my genes?" It is the standard last step after differential expression, a screen, or clustering. There are two core methods, and choosing correctly is the single most important decision:

• **ORA (over-representation analysis)** — take a *thresholded* gene list (e.g., padj < 0.05) and test which gene sets it overlaps more than chance, using Fisher's exact / hypergeometric tests. Tools: Enrichr, g:Profiler.
• **GSEA (gene set enrichment analysis)** — take the *whole ranked list* of genes (no threshold) and test whether each gene set is concentrated toward the top or bottom. Preranked GSEA uses a per-gene score (e.g., the DESeq2 `stat`). Better when effects are broad and subtle.

This skill orchestrates these analyses, the gene-set databases behind them, and the interpretation pitfalls that make results wrong or unpublishable.

When to Use This Skill

Use this skill when the user wants to:

• Find enriched GO terms / KEGG / Reactome / WikiPathways / MSigDB Hallmark sets in a gene list.
• Run GSEA / preranked GSEA on DESeq2, edgeR, limma, or Scanpy `rank_genes_groups` output.
• Score gene-set expression per sample/cell (ssGSEA, GSVA).
• Interpret, deduplicate, and visualize enrichment results, or build a publication table/figure.
• Decide between ORA and GSEA, pick gene-set libraries, choose a background, or fix gene-ID problems.

For quick one-off Enrichr lookups the `gget` skill (`gget enrichr`) is lighter weight; for raw pathway/interaction APIs (Reactome, KEGG, STRING) see the `database-lookup` skill. Use **this** skill for full, defensible enrichment workflows.

Choosing the Right Method

| Situation | Method | Tool / entry point | |-----------|--------|--------------------| | You have a discrete hit list (DE genes, screen hits, cluster markers) | **ORA** | `gp.enrichr(...)` or g:Profiler | | You have a full ranked list (every tested gene + a score) | **Preranked GSEA** | `gp.prerank(...)` | | You have an expression matrix + class labels | **GSEA** | `gp.gsea(...)` | | You want a gene-set score per sample/cell | **ssGSEA / GSVA** | `gp.ssgsea(...)`, `gp.gsva(...)` | | You need a custom background or additional organisms | **ORA with custom domain** | g:Profiler (`domain_scope='custom'`) | | You want TF / signaling *activity* (PROGENy, DoRothEA) | activity inference | see `references/databases-and-gene-sets.md` (decoupler) |

When in doubt: a thresholded list → ORA; a ranked table with scores → GSEA. Never threshold a list and then feed it to GSEA — that discards the ranking GSEA depends on.

Setup

uv pip install gseapy==1.3.1 gprofiler-official==1.0.0 mygene==3.2.2 lxml
# gseapy pulls pandas, numpy, scipy, matplotlib. Network access is needed for
# Enrichr, g:Profiler, and MSigDB downloads. For fully offline ORA, use a local
# GMT file with gp.enrich() (see references/gseapy.md).

The examples target GSEApy 1.3.1 (released stable); the helper runs classic permutation GSEA. Current MSigDB is 2026.1.Hs/2026.1.Mm. Local synthetic workflows and small public mapping/catalog queries were executed; Enrichr submission routes were verified from released source with mocked transport. BioMart returned service-unavailable HTML during review; validate its output schema. See [verified API contracts](references/verified-api.md) for evidence and boundaries.

Verify and list available gene-set libraries (names change over time — never hardcode blindly):

import gseapy as gp
names = gp.get_library_name(organism="human")   # discover current Enrichr libraries
print([n for n in names if "Reactome" in n or "KEGG" in n or "Hallmark" in n])

Quick Start

ORA on a hit list (gseapy + Enrichr)

import gseapy as gp

# Match the library organism and identifier namespace; map aliases explicitly.
# Keep original spelling: capitalization is not gene-ID or orthology mapping.
genes = [g.strip() for g in open("deg_symbols.txt") if g.strip()]
tested = [g.strip() for g in open("tested_symbols.txt") if g.strip()]
assert set(genes) <= set(tested)

enr = gp.enrichr(
    gene_list=genes,
    gene_sets=["MSigDB_Hallmark_2020", "GO_Biological_Process_2026",
               "KEGG_2026", "Reactome_Pathways_2024"],
    organism="human", background=tested,
    outdir=None,            # in-memory; set a path to also write tables/plots
)
res = enr.results
sig = res[res["Adjusted P-value"] < 0.05].sort_values("Adjusted P-value")
print(sig[["Gene_set", "Term", "Adjusted P-value", "Combined Score", "Genes"]].head(20))
# Speedric