commot.tl.communication_deg_detection
- commot.tl.communication_deg_detection(adata, n_var_genes=None, var_genes=None, database_name=None, pathway_name=None, summary='receiver', lr_pair=('total', 'total'), nknots=6, n_deg_genes=None, n_points=50, deg_pvalue_cutoff=0.05)
Identify signaling dependent genes
- adata
The data matrix of shape
n_obs
×n_var
. Rows correspond to cells or positions and columns to genes. The count data should be available through adata.layers[‘count’]. The signaling data should be available in adata.obsm[‘commot-$pathway_name-sum’][‘$summary-$ligand-$receptor’]- n_var_genes
The number of most variable genes to test.
- var_genes
The genes to test. n_var_genes will be ignored if given.
- n_deg_genes
The number of top deg genes to evaluate yhat.
- pathway_name
Name of the signaling pathway.
- summary
‘sender’ or ‘receiver’
- lr_pair
A tuple of the ligand-receptor pair
- nknots
Number of knots in spline when constructing GAM
- n_points
Number of points on which to evaluate the fitted GAM for downstream clustering and visualization.
- deg_pvalue_cutoff
The p-value cutoff of genes for obtaining the fitted gene expression patterns.
- Returns
df_deg (pd.DataFrame) – A data frame of deg analysis results, including Wald statistics, degree of freedom, and p-value.
df_yhat (pd.DataFrame) – A data frame of smoothed gene expression values.