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(pre.res2d[pre.res2d["FDR q-val"] < 0.25].sort_values("NES", ascending=False)
      .head(5)[["Term", "NES", "FDR q-val"]])

Key Parameters

Parameter	Default	Range/Options	Effect
gene_sets (enrichr)	required	string or list	Database name(s) from Enrichr; use gp.get_library_name() to list
organism (enrichr)	"human"	"human", "mouse", "fly", "fish", "worm", "yeast"	Species for gene set lookup
cutoff (enrichr)	0.05	0–1	Adjusted p-value cutoff for filtering results
rnk (prerank)	required	pd.Series	Gene → score mapping; sorted descending (log2FC recommended)
permutation_num (prerank)	1000	100–10000	Permutations for p-value estimation; 1000 for publication
min_size (prerank)	15	5–50	Minimum gene set size; filters small/poorly characterized sets
max_size (prerank)	500	100–2000	Maximum gene set size; filters very large generic sets
threads (prerank)	4	1–64	CPU threads for permutation
seed (prerank)	None	integer	Random seed for reproducibility
weighted_score_type (prerank)	1	0, 1, 1.5	GSEA weighting; 1 = standard weighted GSEA

Common Recipes

Recipe 1: Compare Enrichment Between Two Conditions

import gseapy as gp
import pandas as pd

conditions = {
    "treated_vs_ctrl": ["TP53", "BRCA1", "CDK2", "CCND1", "MYC"],
    "treated2_vs_ctrl": ["EGFR", "KRAS", "PTEN", "RB1", "AKT1"],
}

results = {}
for label, genes in conditions.items():
    enr = gp.enrichr(gene_list=genes,
                     gene_sets=["MSigDB_Hallmark_2020"],
                     organism="human",
                     outdir=None)
    sig = enr.results[enr.results["Adjusted P-value"] < 0.05]
    results[label] = set(sig["Term"])
    print(f"{label}: {len(sig)} significant Hallmark terms")

# Overlap
shared = results["treated_vs_ctrl"] & results["treated2_vs_ctrl"]
print(f"Shared terms: {shared}")

Recipe 2: Batch Prerank for Multiple Comparisons

import gseapy as gp
import pandas as pd
from pathlib import Path

# Load multiple DESeq2 result files
comparisons = {
    "treat_vs_ctrl": "deseq_treat_vs_ctrl.tsv",
    "drug_vs_ctrl": "deseq_drug_vs_ctrl.tsv",
}

for name, file in comparisons.items():
    # df = pd.read_csv(file, sep="\t", index_col=0)
    # ranked = df["log2FoldChange"].dropna().sort_values(ascending=False)
    
    # Example: generate synthetic ranked list
    import numpy as np
    ranked = pd.Series(np.random.normal(0, 1, 800),
                       index=[f"G{i}" for i in range(800)]).sort_values(ascending=False)
    
    pre = gp.prerank(
        rnk=ranked,
        gene_sets=["MSigDB_Hallmark_2020", "KEGG_2021_Human"],
        threads=4,
        permutation_num=500,
        outdir=f"gsea_results/{name}/",
        seed=42,
    )
    sig = pre.res2d[pre.res2d["FDR q-val"] < 0.25]
    print(f"{name}: {len(sig)} significant gene sets")
    pre.res2d.to_csv(f"gsea_results/{name}/all_results.tsv", sep="\t")

Expected Outputs

Output	Format	Description
enr.results	DataFrame	ORA results: Term, P-value, Adjusted P-value, Combined Score, Genes
pre_res.res2d	DataFrame	Prerank results: Term, ES, NES, NOM p-val, FDR q-val, Gene %
gsea_results/*.csv	CSV	Saved enrichment tables per database
gsea_results/*.pdf	PDF	GSEA running-score plots (one per gene set)
enrichment_dotplot.png	PNG	Dot plot of top enriched terms
gseaplot output	PNG/PDF	Running enrichment score + ranked list plot

Troubleshooting

Problem	Cause	Solution
ConnectionError in enrichr	No internet or Enrichr API down	Check https://maayanlab.cloud/Enrichr/; use local gene sets with gene_sets="path/to/gmt"
No significant terms returned	Gene list too small or wrong gene ID format	Use ≥10 genes; ensure HGNC symbols (not Ensembl IDs); convert with pyensembl
Prerank returns all NES ≈ 0	Ranked list not sorted or too few genes	Verify rnk is sorted descending; check min_size ≤ gene set sizes
KeyError in gene set	Gene set name misspelled	Use gp.get_library_name() to get exact database names
Low NES with FDR > 0.25	Signal is weak or permutation count too low	Increase permutation_num to 1000; check raw p-values in NOM p-val
GSEA plot shows flat line	Gene set has no intersection with ranked list	Check gene naming; confirm gene set species matches data
Memory error during prerank	Large expression matrix + high permutations	Reduce permutation_num; use prerank instead of gsea when possible
Enrichr results differ from Java GSEA	Different gene set versions	Specify exact database version string from gp.get_library_name()

References

• GSEApy documentation — official usage guide and API reference
• GSEApy GitHub: zqfang/GSEApy — source code and examples
• Fang Z et al. (2023) "GSEApy: a comprehensive package for performing gene set enrichment analysis in Python" — Bioinformatics 39(1):btac757. DOI:10.1093/bioinformatics/btac757
• Subramanian A et al. (2005) "Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles" — PNAS 102(43):15545-15550. DOI:10.1073/pnas.0506580102
• Enrichr gene set databases — full list of 200+ available gene set libraries