gcell-pathway
Pathway enrichment analysis using gcell. Use this skill when users ask about: - Gene set enrichment analysis - GO (Gene Ontology) enrichment - KEGG pathway analysis - Reactome pathway enrichment - Custom pathway/gene set analysis Triggers: pathway enrichment, GO enrichment, KEGG, Reactome, gene set analysis, functional enrichment, ontology
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill instructions
Pathway Enrichment Analysis
Quick Enrichment with gprofiler
from gcell.ontology.pathway import gprofiler_enrichment
# Basic enrichment analysis
gene_list = ['TP53', 'BRCA1', 'MYC', 'EGFR', 'KRAS']
results = gprofiler_enrichment(gene_list, organism='hsapiens')
# Specify data sources
results = gprofiler_enrichment(
gene_list,
organism='hsapiens',
sources=['GO:BP', 'GO:MF', 'GO:CC', 'KEGG', 'REAC']
)
# Sources available:
# - GO:BP (Biological Process)
# - GO:MF (Molecular Function)
# - GO:CC (Cellular Component)
# - KEGG (KEGG pathways)
# - REAC (Reactome)
# - WP (WikiPathways)
# - TF (Transcription factors)
# - MIRNA (microRNA targets)
# - HPA (Human Protein Atlas)
# - CORUM (Protein complexes)
# - HP (Human Phenotype Ontology)
Working with Results
# Results is a pandas DataFrame
print(results.columns)
# ['source', 'term_id', 'term_name', 'p_value', 'significant',
# 'term_size', 'query_size', 'intersection_size', 'intersections']
# Filter significant results
significant = results[results['p_value'] < 0.05]
# Sort by p-value
top_terms = results.sort_values('p_value').head(20)
# Get genes in each term
for _, row in top_terms.iterrows():
print(f"{row['term_name']}: {row['intersections']}")
Mouse and Other Organisms
# Mouse
results = gprofiler_enrichment(gene_list, organism='mmusculus')
# Rat
results = gprofiler_enrichment(gene_list, organism='rnorvegicus')
# Other organisms: use Ensembl species codes
Custom Pathways from GMT Files
from gcell.ontology.pathway import Pathways
# Load custom gene sets from GMT file
pathways = Pathways.from_gmt('custom_pathways.gmt')
# Run enrichment against custom pathways
background_genes = [...] # All expressed genes
enriched = pathways.enrichment(gene_list, background_genes)
Key Functions and Classes
| Name | Purpose |
|---|---|
gprofiler_enrichment() |
Quick enrichment via g:Profiler |
Pathways |
Custom pathway collections |
Pathways.from_gmt() |
Load GMT format gene sets |
Pathways.enrichment() |
Run enrichment analysis |
Tips
- Always use appropriate background genes when possible
- Multiple testing correction is applied automatically
- Use specific sources (e.g., just 'GO:BP') to reduce multiple testing burden
- Gene symbols should match the organism (human: HUGO symbols)
Examples
Read the usage examples in the source repository.
View source ↗Source history
- Indexed on
- 2026-01-20
- Index updated
- 2026-01-26
- Branch
- main
Index and sync dates describe this directory. View the source repository for code history.
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