GSEApy — Gene Set Enrichment Analysis in Python

Overview

GSEApy provides Python implementations of GSEA and over-representation analysis (ORA) for interpreting gene expression changes at the pathway level. The enrich module queries the Enrichr API to test a gene list against 200+ databases (GO, KEGG, MSigDB Hallmarks, Reactome, WikiPathways). The prerank and gsea modules run the GSEA algorithm on a pre-ranked gene list or expression matrix — computing normalized enrichment scores (NES) and FDR values for each gene set. GSEApy integrates directly with pandas DataFrames from DESeq2 or scanpy differential expression output, making it the standard Python tool for pathway analysis in RNA-seq workflows.

When to Use

• Interpreting DESeq2 or edgeR differential expression results at pathway/GO-term level
• Running fast ORA (over-representation analysis) against Enrichr's 200+ databases including GO, KEGG, and MSigDB Hallmarks
• Performing GSEA prerank analysis on a log2-fold-change-ranked gene list without an expression matrix
• Identifying enriched pathways in scRNA-seq cluster marker genes
• Generating publication-ready enrichment dot plots and GSEA running-score plots
• Use GSEA Java application for the official GUI-based analysis with full GSEA desktop interface
• Use fgsea (R) as an alternative with fast permutation-based p-values; GSEApy is preferred for Python-native pipelines

Prerequisites

• Python packages: gseapy, pandas, matplotlib
• Internet access: enrich module queries the Enrichr API (requires connection)

pip install gseapy

# Verify
python -c "import gseapy; print(gseapy.__version__)"
# 1.1.3

Quick Start

import gseapy as gp

# ORA: test a gene list against GO Biological Process
gene_list = ["TP53", "BRCA1", "CDK2", "CCND1", "MYC", "EGFR", "KRAS", "PTEN"]

enr = gp.enrichr(gene_list=gene_list,
                 gene_sets=["GO_Biological_Process_2023"],
                 organism="human",
                 outdir=None)
print(enr.results.head(5)[["Term", "P-value", "Adjusted P-value", "Genes"]])

Workflow

Step 1: Over-Representation Analysis with Enrichr (ORA)

Test a gene list against pathway databases via the Enrichr API.

import gseapy as gp
import pandas as pd

# Gene list from DESeq2 (significant upregulated genes)
sig_genes = ["TP53", "BRCA1", "CDK2", "CCND1", "MYC", "EGFR",
             "KRAS", "PTEN", "RB1", "AKT1", "PIK3CA", "MDM2"]

# Run ORA against multiple databases
enr = gp.enrichr(
    gene_list=sig_genes,
    gene_sets=[
        "GO_Biological_Process_2023",
        "KEGG_2021_Human",
        "MSigDB_Hallmark_2020",
        "Reactome_2022",
    ],
    organism="human",
    outdir="enrichr_results/",
    cutoff=0.05,
)

# Display top results
results = enr.results
print(f"Enriched terms: {len(results[results['Adjusted P-value'] < 0.05])}")
print(results[results["Adjusted P-value"] < 0.05].sort_values("Adjusted P-value")
      .head(10)[["Gene_set", "Term", "Adjusted P-value", "Combined Score"]])

Step 2: List Available Gene Set Databases

Discover the 200+ databases available through Enrichr.

import gseapy as gp

# List all available gene set libraries
libraries = gp.get_library_name(organism="human")
print(f"Available databases: {len(libraries)}")
print("Selected databases:")
for lib in sorted(libraries):
    if any(kw in lib for kw in ["GO_Bio", "KEGG", "Hallmark", "Reactome"]):
        print(f"  {lib}")

# Mouse databases
mouse_libs = gp.get_library_name(organism="mouse")
print(f"\nMouse databases: {len(mouse_libs)}")

Step 3: GSEA Prerank — Ranked Gene List Analysis

Run GSEA on a log2 fold-change ranked gene list from differential expression.

import gseapy as gp
import pandas as pd
import numpy as np

# Load DESeq2 results (or create example ranked list)
# deseq_results = pd.read_csv("deseq2_results.tsv", sep="\t", index_col=0)
# ranked = deseq_results["log2FoldChange"].dropna().sort_values(ascending=False)

# Example ranked gene list (gene → log2FC)
np.random.seed(42)
gene_names = [f"GENE_{i}" for i in range(1000)]
log2fc = np.random.normal(0, 2, 1000)
ranked = pd.Series(log2fc, index=gene_names).sort_values(ascending=False)

# Run preranked GSEA against MSigDB Hallmarks
pre_res = gp.prerank(
    rnk=ranked,
    gene_sets="MSigDB_Hallmark_2020",
    threads=4,
    min_size=15,
    max_size=500,
    permutation_num=1000,
    outdir="gsea_results/prerank/",
    seed=42,
    verbose=True,
)

# View results
res_df = pre_res.res2d
sig = res_df[res_df["FDR q-val"] < 0.25]
print(f"Significant gene sets (FDR < 0.25): {len(sig)}")
print(sig.sort_values("NES", ascending=False)[["Term", "NES", "NOM p-val", "FDR q-val"]].head(10))

Step 4: Plot GSEA Running Score

Visualize the enrichment score curve for a specific gene set.

import gseapy as gp
from gseapy.plot import gseaplot
import matplotlib.pyplot as plt

# Re-use pre_res from Step 3 (or load saved results)
# Select the top enriched gene set
top_term = pre_res.res2d.sort_values("NES", ascending=False).index[0]
print(f"Top enriched gene set: {top_term}")

# Plot running enrichment score
ax = gseaplot(
    rank_metric=pre_res.ranking,
    term=top_term,
    **pre_res.results[top_term],
    ofname="gsea_results/top_geneset_enrichment.pdf",
)
plt.tight_layout()
plt.savefig("gsea_enrichment_plot.png", dpi=150)
print("Saved: gsea_enrichment_plot.png")

Step 5: Enrichment Dot Plot for Multiple Terms

Generate a dot plot showing enrichment significance and gene ratio across top pathways.

import gseapy as gp
import matplotlib.pyplot as plt
from gseapy.plot import dotplot

# Run ORA and plot results
enr = gp.enrichr(
    gene_list=["TP53", "BRCA1", "CDK2", "CCND1", "MYC", "EGFR",
               "KRAS", "PTEN", "RB1", "AKT1", "PIK3CA", "MDM2",
               "BCL2", "CDKN1A", "E2F1", "CCNE1"],
    gene_sets=["KEGG_2021_Human"],
    organism="human",
    outdir=None,
    cutoff=0.05,
)

# Dot plot: x=gene ratio, size=-log10(p), color=adjusted p-value
ax = dotplot(
    enr.results,
    column="Adjusted P-value",
    x="Gene_set",
    title="KEGG Enrichment",
    cmap="viridis_r",
    size=10,
    top_term=15,
    figsize=(6, 8),
    ofname="enrichment_dotplot.pdf",
)
plt.tight_layout()
plt.savefig("enrichment_dotplot.png", dpi=150, bbox_inches="tight")
print("Saved: enrichment_dotplot.png")

Step 6: Integrate with DESeq2 / scanpy Output

Use GSEApy directly on differential expression results.

import gseapy as gp
import pandas as pd

# From DESeq2 output loaded into Python
# deseq_df = pd.read_csv("deseq2_results.tsv", sep="\t", index_col=0)
# deseq_df = deseq_df.dropna(subset=["log2FoldChange", "padj"])

# Simulate DESeq2 output
import numpy as np
np.random.seed(0)
n = 500
deseq_df = pd.DataFrame({
    "log2FoldChange": np.random.normal(0, 1.5, n),
    "padj": np.random.uniform(0, 1, n),
}, index=[f"GENE{i}" for i in range(n)])

# Significant up/down gene lists for ORA
up_genes = deseq_df[(deseq_df["padj"] < 0.05) & (deseq_df["log2FoldChange"] > 1)].index.tolist()
dn_genes = deseq_df[(deseq_df["padj"] < 0.05) & (deseq_df["log2FoldChange"] < -1)].index.tolist()
print(f"Upregulated: {len(up_genes)}, Downregulated: {len(dn_genes)}")

# ORA on upregulated genes
if up_genes:
    enr_up = gp.enrichr(gene_list=up_genes,
                         gene_sets=["GO_Biological_Process_2023", "KEGG_2021_Human"],
                         organism="human", outdir=None)
    sig_up = enr_up.results[enr_up.results["Adjusted P-value"] < 0.05]
    print(f"Enriched terms (upregulated): {len(sig_up)}")
    print(sig_up.sort_values("Adjusted P-value").head(5)[["Term", "Adjusted P-value"]])

# Preranked GSEA on full ranked list
ranked = deseq_df["log2FoldChange"].sort_values(ascending=False)
pre = gp.prerank(rnk=ranked, gene_sets="MSigDB_Hallmark_2020",
                 threads=4, permutation_num=500, outdir="gsea_out/", seed=42)
print