| name | gcell-celltype |
| description | 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
|
Cell Type Regulatory Analysis
Loading Cell Types
from gcell.cell.celltype import GETDemoLoader
loader = GETDemoLoader()
print(loader.available_celltypes)
ct = loader.load_celltype('Plasma Cell')
ct = loader.load_celltype('CD4+ T Cell')
ct = loader.load_celltype('Monocyte')
Gene-by-Motif Analysis
The gene-by-motif matrix shows how transcription factor motifs influence gene expression in a cell type.
gbm = ct.get_gene_by_motif()
print(gbm.shape)
print(gbm.loc['MYC'])
Gene Jacobian Analysis
Jacobian analysis reveals which regulatory elements most influence a gene's expression.
jacobian = ct.get_gene_jacobian_summary('MYC')
jacobian = ct.get_gene_jacobian_summary('TP53')
print(jacobian.sort_values(ascending=False).head(20))
Motif Subnet Visualization
Visualize the regulatory network around a specific motif/TF.
ct.plotly_motif_subnet(motif_name='STAT3', top_genes=20)
ct.plotly_motif_subnet(motif_name='PU.1', top_genes=30)
ct.plotly_motif_subnet(motif_name='GATA1', top_genes=15)
Key Classes
| Class | Purpose |
|---|
GETDemoLoader | Load pre-inferred cell types |
GETCellType | Cell type analysis container |
GETHydraCellType | Multi-cell type analysis |
Data Location
Pre-inferred cell type data is downloaded automatically to ~/.gcell_data/ on first use.