Pathway Enrichment

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

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 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

# Enrichr libraries expect HGNC gene SYMBOLS (human: UPPERCASE). Map IDs first if needed.
genes = [g.strip() for g in open("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 \