gcell-pathway

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التفرعات٩

آخر تحديث١٦ يناير ٢٠٢٦ في ٠٧:٢١

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

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

المصدر

GET-Foundation

GET-Foundation/gcell

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

علماء الأحياء، جميع الآخرونعلوم الحياة والطبيعة والاجتماع·SOC 19-1029

SKILL.md

readonly

name	gcell-pathway
description	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

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)

المزيد من هذا المستودع

نفس المستودع

gcell-chipatlas

GET-Foundation/gcell

ChIP-Atlas epigenome data query using gcell. Use this skill when users ask about: - Finding ChIP-seq, ATAC-seq, or DNase-seq experiments - Searching for transcription factor binding data - Finding histone modification data (H3K27ac, H3K4me3, etc.) - Querying epigenome data by cell type or tissue - Downloading peak files or BigWig coverage data - Enrichment analysis for genomic regions or gene lists Triggers: ChIP-seq, ChIP-Atlas, ATAC-seq, DNase-seq, histone modification, H3K27ac, H3K4me3, transcription factor binding, epigenome, peak data, BigWig, enrichment analysis

2026-01-1814

gcell-gene

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Gene annotations and TSS analysis using gcell. Use this skill when users ask about: - GENCODE gene annotations - Transcription start sites (TSS) - Gene coordinates and metadata - Transcript information - Querying genes by genomic region Triggers: gene annotation, GENCODE, TSS, transcription start site, gene coordinates, transcript, GTF

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gcell-causal

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Causal network analysis using gcell. Use this skill when users ask about: - Inferring causal relationships from expression data - LiNGAM causal discovery algorithm - Regulatory network construction - Visualizing causal/regulatory networks Triggers: causal network, LiNGAM, causal inference, regulatory network, causal discovery, gene regulatory network

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gcell-celltype

GET-Foundation/gcell

Cell type regulatory analysis using gcell. Use this skill when users ask about: - Loading pre-inferred cell types (GET model outputs) - Gene-by-motif matrices showing TF influence on genes - Gene Jacobian analysis for regulatory importance - Motif subnet visualization - Cell type-specific gene expression patterns Triggers: cell type, regulatory analysis, gene expression, Jacobian, motif subnet, GET model, TF influence

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gcell-dna

GET-Foundation/gcell

DNA sequence and motif analysis using gcell. Use this skill when users ask about: - Loading and manipulating DNA sequences - Genome assembly access (hg38, hg19, mm10) - Motif scanning with HOCOMOCO motifs - Working with genomic regions (BED, narrowPeak files) - Transcription factor binding site prediction Triggers: DNA sequence, motif scanning, HOCOMOCO, genomic regions, BED file, narrowPeak, genome, transcription factor binding

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gcell-protein

GET-Foundation/gcell

Protein structure and interaction analysis using gcell. Use this skill when users ask about: - Protein sequences from gene names - AlphaFold2 structure predictions and pLDDT scores - UniProt protein information - 3D protein structure visualization - Protein-protein interactions (STRING database) Triggers: protein structure, AlphaFold, pLDDT, UniProt, protein sequence, 3D structure, protein interaction, STRING

2026-01-1614

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)