菜单
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
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基于 SOC 职业分类
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
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
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
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
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
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
| 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 |
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)
# 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
results = gprofiler_enrichment(gene_list, organism='mmusculus')
# Rat
results = gprofiler_enrichment(gene_list, organism='rnorvegicus')
# Other organisms: use Ensembl species codes
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)
| 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 |