liana.method.estimate_metalinks

liana.method.estimate_metalinks(adata, resource, pd_net, t_net=None, x_name='metabolite', y_name='receptor', use_raw=True, layer=None, verbose=False, **kwargs)

Estimate Metabolites from anndata object, and return a MuData object of metabolites and receptors.

Parameters:

• adata (AnnData) – Annotated data matrix.

• resource (DataFrame) – Resource to use for ligand-receptor inference.

• pd_net (DataFrame) – Metabolic production-degradation network to use.

• t_net (DataFrame (default: None)) – Transport set to use.

• x_name (str (default: 'metabolite')) – Name of the metabolite modality.

• y_name (str (default: 'receptor')) – Name of the receptor modality. Must be present as a column in the resource.

• use_raw (bool (default: True)) – Use raw attribute of adata if present.

• layer (str (default: None)) – Layer in anndata.AnnData.layers to use. If None, use anndata.AnnData.X.

• verbose (bool (default: False)) – Verbosity flag.

• **kwargs – Additional arguments to pass to the decoupler-py functions. Method-specific arguments are not supported.

Return type:

MuData

Returns:

A MuData object with metabolite & receptor assays.

Examples

pd_net is a metabolite-to-enzyme network whose weights say whether a gene produces (+) or degrades (-) a metabolite, and resource links metabolites to their receptors. Both normally come from MetalinksDB (see liana.resource.get_metalinks()); toy ones are built here so the example stays offline:

>>> import numpy as np
>>> import pandas as pd
>>> import liana as li
>>> adata = li.testing.generate_toy_adata()
>>> genes = adata.var_names[:16].tolist()
>>> pd_net = pd.DataFrame({'source': np.repeat(['HMDB0000122',
...                                            'HMDB0000148'], 8),
...                        'target': genes,
...                        'weight': 1.0})
>>> resource = pd.DataFrame({'source': ['HMDB0000122', 'HMDB0000148'],
...                          'receptor': ['CD4', 'ITGB2']})
>>> mdata = li.mt.estimate_metalinks(adata, resource=resource, pd_net=pd_net)

Metabolite abundances are estimated from the enzyme expression and returned in a 'metabolite' modality, next to the receptors in a 'receptor' one. Pass t_net to additionally require a transporter for metabolites that cannot cross the membrane on their own. The result is the input to liana.method.bivariate or to any single-cell method, with x_mod='metabolite' and y_mod='receptor'.