Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever 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".
Instrucciones de origen · Vista previa de solo lectura
name
pathway-enrichment
description
Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever 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
metadata
{"version":"1.0","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 pathway activity 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 pathway score per sample/cell
ssGSEA / GSVA
gp.ssgsea(...), gp.gsva(...)
You need a custom background or 500+ 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 gprofiler-official
# 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).
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") # 200+ Enrichr librariesprint([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
# Enrichr libraries expect HGNC gene SYMBOLS (human: UPPERCASE). Map IDs first if needed.
genes = [g.strip() for g inopen("deg_symbols.txt") if g.strip()]
enr = gp.enrichr(
gene_list=genes,
gene_sets=["MSigDB_Hallmark_2020", "GO_Biological_Process_2023",
"KEGG_2021_Human", "Reactome_2022"],
organism="human",
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", "Overlap", "Adjusted P-value", "Combined Score", "Genes"]].head(20))
Preranked GSEA from DESeq2 results
import gseapy as gp
import pandas as pd
res = pd.read_csv("deseq2_results.csv", index_col=0) # index = gene symbols# Rank by the test statistic (sign = direction, magnitude = evidence). This is# more stable than ranking by log2FoldChange, which is noisy for low-count genes.
rnk = res["stat"].dropna().sort_values(ascending=False)
rnk.index = rnk.index.str.upper()
rnk = rnk[~rnk.index.duplicated(keep="first")]
pre = gp.prerank(
rnk=rnk,
gene_sets=["MSigDB_Hallmark_2020", "GO_Biological_Process_2023"],
min_size=15, max_size=500, # drop tiny/huge sets (noisy or generic)
permutation_num=1000, seed=123, # seed = reproducible p-values
threads=4, outdir=None,
)
out = pre.res2d.sort_values("FDR q-val")
print(out[["Term", "ES", "NES", "NOM p-val", "FDR q-val", "Lead_genes"]].head(20))
If you have no stat column, build the rank from sign(log2FoldChange) * -log10(pvalue).
Core Workflow
For a defensible analysis, work through these steps. The middle steps (ID type, background) are where results most often silently go wrong.
Step 1 — Pin down inputs and pick the method
Confirm: which genes, what organism, is there a per-gene score (→ GSEA) or just a list (→ ORA), and what comparison they represent (direction matters for interpretation).
Step 2 — Get gene IDs into the right namespace
Enrichr/MSigDB libraries are keyed by gene symbols (human UPPERCASE, mouse Title-case). If you have Ensembl/Entrez IDs, convert first. See references/databases-and-gene-sets.md for gp.Biomart, g:Profiler g:Convert, and mygene. A silent ID mismatch is the #1 cause of "nothing is significant".
Step 3 — Choose gene-set libraries to match the question
Hallmark (broad themes) → GO:BP (mechanism) → KEGG/Reactome/WikiPathways (curated pathways) → C7 (immune), etc. Don't run 50 libraries; pick 2–4 that fit the biology. Catalog and selection guidance: references/databases-and-gene-sets.md.
Step 4 — Set the background universe (ORA only)
The background must be the genes that could have been detected in your assay (e.g., all expressed/tested genes), not the whole genome. The wrong background inflates significance. Enrichr uses a fixed background; when background matters, use g:Profiler with domain_scope='custom' + your background, or gp.enrich() with an explicit background. Rationale in references/interpretation.md.
Step 5 — Run the analysis
Use the Quick Start patterns or the bundled scripts/run_enrichment.py. For GSEA always set a seed and report permutation_num.
Step 6 — Filter on adjusted p-values
Use Adjusted P-value (ORA, Benjamini–Hochberg) or FDR q-val (GSEA), not raw p-values. Typical cutoff 0.05; also check the overlap/gene count so a "hit" isn't 1 gene out of a 2000-gene set.
Step 7 — Visualize
Dotplots, bar plots, enrichment maps, and GSEA running-score plots are built into gseapy (gp.dotplot, gp.barplot, gp.enrichment_map, gp.gseaplot). See references/gseapy.md.
Step 8 — Reduce redundancy and interpret
GO especially returns many near-duplicate terms. Collapse with an enrichment map (term–term similarity), leading-edge overlap, or parent terms, and report representative terms. Interpretation framework and a publication-table format are in references/interpretation.md.
Helper Script
scripts/run_enrichment.py runs ORA or GSEA end-to-end and writes a results table plus a dotplot, handling the boilerplate (symbol cleanup, dedup, NA removal, rank construction from a DESeq2 table, per-library FDR filtering).
# ORA from a hit list (one gene symbol per line)
python scripts/run_enrichment.py ora \
--genes deg_symbols.txt \
--libraries MSigDB_Hallmark_2020 GO_Biological_Process_2023 KEGG_2021_Human \
--organism human --outdir results/
# Preranked GSEA from a DESeq2 results CSV (auto-builds the rank from `stat`)
python scripts/run_enrichment.py gsea \
--deseq2 deseq2_results.csv \
--libraries MSigDB_Hallmark_2020 GO_Biological_Process_2023 \
--organism human --outdir results/ --seed 123
# Preranked GSEA from an explicit 2-column rank file (gene,score)
python scripts/run_enrichment.py gsea --rnk ranked_genes.csv --outdir results/
Run python scripts/run_enrichment.py --help for all options (background file, FDR cutoff, min/max set size, permutations).
Common Pitfalls
These cause most wrong or irreproducible results:
Gene-ID / organism mismatch — symbols vs Ensembl, human vs mouse casing. Map IDs and set organism correctly, or matches silently drop to ~zero.
Wrong background (ORA) — using the whole genome instead of the tested/expressed gene set inflates p-values. Set a custom background when it matters.
Thresholding before GSEA — GSEA needs the full ranked list; only ORA uses a cut list.
Ranking GSEA by log2FoldChange alone — unstable for low-count genes; prefer stat or sign(LFC) * -log10(p).
Multiple-testing across libraries — FDR is computed within a library; running many libraries multiplies tests. Report per-library FDR and stay conservative.
Redundant GO terms — don't report 40 variants of the same term; collapse and show representatives.
Significance ≠ relevance — check the overlap count and gene-set size; tiny sets reach significance trivially.
List too short/long for ORA — <10 genes is underpowered; >2000 loses specificity (consider GSEA instead).
No reproducibility metadata — Enrichr/GO libraries are versioned and drift over time. Record library names+date and set a GSEA seed.
Integration with Other Skills
Upstream (where genes come from):pydeseq2 (DE genes + stat for GSEA), scanpy (rank_genes_groups markers / scores), depmap/pytdc (screen hits), proteomics skills (pyopenms, matchms).
Databases / IDs:database-lookup (Reactome, KEGG, STRING, Gene Ontology APIs), gget (gget enrichr quick path, gget info for ID mapping), bioservices.
references/gseapy.md — full gseapy API: enrichr, offline enrich, prerank, gsea, ssgsea, gsva, Msigdb, Biomart, get_library_name/read_gmt, every plot, result-column meanings, GMT/offline usage, and troubleshooting (rate limits, empty results).
references/databases-and-gene-sets.md — GO, KEGG, Reactome, WikiPathways, MSigDB collections, Enrichr library naming, g:Profiler sources, organism handling, gene-ID conversion, library selection by question, and pointers to Reactome/STRING APIs and decoupler activity inference.
references/interpretation.md — ORA vs GSEA statistics, background-universe choice, multiple-testing methods (BH vs g:SCS vs Bonferroni), leading-edge genes, redundancy reduction, effect vs significance, a publication-table template, and reproducibility checklist.
