Rechercher

PythonReactSEOPDF
GitHub Skill

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

GET-Foundation☆ 12 Étoiles du dépôt9 Forks du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

Voir la source ↗Guides

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)

Exemples

Consultez les exemples du dépôt source.

Voir la source ↗

Historique source

Date d’indexation
2026-01-20
Mise à jour de l’index
2026-01-26
Branch
main

Les dates concernent ce répertoire. Consultez le dépôt pour l’historique du code.

GitHub ↗

chat Comments (0)

chat_bubble_outline

No comments yet. Be the first to share your thoughts!