gsea-enrichment-analysis

GSEA and Pathway Enrichment Analysis

Overview

This skill covers gene set enrichment analysis (GSEA) and pathway enrichment workflows in OmicVerse. It provides critical guidance on the correct data formats and API usage patterns to avoid common errors.

Critical API Reference - Geneset Format

IMPORTANT: Use Dictionary Format, NOT File Path!

The ov.bulk.geneset_enrichment() function requires a dictionary of gene sets, NOT a file path string. You must first load the geneset file using ov.utils.geneset_prepare().

CORRECT usage:

# Step 1: Download pathway database (if not already available)
ov.utils.download_pathway_database()

# Step 2: Load geneset file into dictionary format - REQUIRED!
pathways_dict = ov.utils.geneset_prepare(
    'genesets/GO_Biological_Process_2021.txt',  # or .gmt file
    organism='Human'  # or 'Mouse'
)

# Step 3: Now run enrichment with the DICTIONARY
enr = ov.bulk.geneset_enrichment(
    gene_list=deg_genes,
    pathways_dict=pathways_dict,  # Pass the DICTIONARY, not file path!
    pvalue_type='auto',
    organism='Human'
)

WRONG - DO NOT USE:

# WRONG! Don't pass file path directly to geneset_enrichment!
# enr = ov.bulk.geneset_enrichment(
#     gene_list=deg_genes,
#     pathways_dict='genesets/GO_Biological_Process_2021.gmt'  # ERROR! String path doesn't work!
# )

# WRONG! geneset_enrichment expects dict, not file path
# enr = ov.bulk.geneset_enrichment(
#     gene_list=deg_genes,
#     pathways_dict='GO_Biological_Process_2021'  # ERROR!
# )

File Format Support

File Extension	Load Method	Notes
.txt	ov.utils.geneset_prepare()	OmicVerse format
.gmt	ov.utils.geneset_prepare()	Standard GMT format
.json	json.load() then convert	Custom handling needed

Complete Enrichment Workflow

import omicverse as ov

# 1. Setup
ov.plot_set()

# 2. Ensure pathway database is available
ov.utils.download_pathway_database()

# 3. Load gene sets - ALWAYS use geneset_prepare first!
go_bp = ov.utils.geneset_prepare('genesets/GO_Biological_Process_2021.txt', organism='Human')
go_mf = ov.utils.geneset_prepare('genesets/GO_Molecular_Function_2021.txt', organism='Human')
kegg = ov.utils.geneset_prepare('genesets/KEGG_2021_Human.txt', organism='Human')

# 4. Prepare gene list (e.g., from DEG analysis)
# Assuming dds is a pyDEG object with results
deg_genes = dds.result.loc[dds.result['sig'] != 'normal'].index.tolist()

# 5. Run enrichment with dictionary
enr_go_bp = ov.bulk.geneset_enrichment(
    gene_list=deg_genes,
    pathways_dict=go_bp,  # Dictionary, NOT file path!
    pvalue_type='auto',
    organism='Human'
)

# 6. Visualize results
ov.bulk.geneset_plot(enr_go_bp, figsize=(6, 8), num=10)

# 7. For multiple databases, combine into dict
enr_dict = {
    'GO_BP': enr_go_bp,
    'GO_MF': enr_go_mf,
    'KEGG': enr_kegg
}
colors_dict = {
    'GO_BP': '#1f77b4',
    'GO_MF': '#ff7f0e',
    'KEGG': '#2ca02c'
}
ov.bulk.geneset_plot_multi(enr_dict, colors_dict, num=5)

Common Errors and Solutions

Error: "FileNotFoundError" or "pathways_dict is not a dict"

Cause: Passing file path string instead of dictionary to geneset_enrichment() Solution: First load with ov.utils.geneset_prepare(), then pass the returned dictionary

Error: "Missing file 'genesets/GO_Biological_Process_2021.gmt'"

Cause: Pathway database not downloaded Solution: Run ov.utils.download_pathway_database() first

Error: "No enriched pathways found"

Cause: Gene list doesn't overlap with pathway genes, or organism mismatch Solution:

• Verify gene symbols match (human vs mouse capitalization)
• Check organism parameter matches your data
• Ensure gene list has sufficient genes (>10 recommended)

Pathway Databases Available

After running ov.utils.download_pathway_database():

• GO_Biological_Process_2021.txt
• GO_Molecular_Function_2021.txt
• GO_Cellular_Component_2021.txt
• KEGG_2021_Human.txt
• KEGG_2021_Mouse.txt
• Reactome_2022.txt
• WikiPathway_2023_Human.txt
• And many more...

Best Practices

1. Always load genesets first: Never pass file paths directly to geneset_enrichment()
2. Check gene format: Ensure gene symbols match (CAPS for human, Title case for mouse)
3. Download once: Run download_pathway_database() once per environment
4. Specify organism: Always set organism='Human' or organism='Mouse'
5. Use background genes: For more accurate results, provide background parameter

Examples

• "Run GO enrichment on my DEG results using the correct geneset_prepare workflow"
• "Perform KEGG pathway analysis on upregulated genes with proper dictionary format"
• "Compare GO BP, MF, and KEGG enrichment results using geneset_plot_multi"

References

• Tutorial notebook: t_deg.ipynb (enrichment section)
• Pathway download: ov.utils.download_pathway_database()
• Quick reference: reference.md