metachat.tl.communication_responseGenes

metachat.tl.communication_responseGenes(adata, adata_raw, database_name=None, metabolite_name=None, metapathway_name=None, customerlist_name=None, ms_pairs_name=None, group_name=None, subgroup=None, summary='receiver', var_genes=None, n_deg_genes=None, nknots=6, n_points=50, deg_pvalue_cutoff=0.05)

Identify signal-dependent genes responding to MCC communication patterns.

Parameters

adataanndata.AnnData

adata.AnnData object after running inference function mc.tl.metabolic_communication.

adata_rawanndata.AnnData

adata.AnnData object with raw spatial transcriptome data.

database_namestr

Name of the Metabolite-Sensor interaction database.

metabolite_namestr, optional

Name of a specific metabolite to detect response genes. For example, metabolite_name = ‘HMDB0000148’.

metapathway_namestr, optional

Name of a specific metabolic pathways to detect response genes. For example, metabolite_name = ‘Alanine, aspartate and glutamate metabolism’.

customerlist_namestr, optional

Name of a specific customerlist to detect response genes. For example, customerlist_name = ‘CustomerA’.

ms_pairs_namestr, optional

Name of a specific metabolite-sensor pairs to detect response genes. For example, ms_pairs_name = ‘HMDB0000148-Grm5’.

group_namestr, optional

Grouping column name in adata.obs for selecting subgroups.

subgrouplist, optional

Subset of groups to include.

summary{‘sender’, ‘receiver’}, default=’receiver’

Specify whether to analyze sender or receiver side.

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.

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.