SpaRCL.regulons

SpaRCL.regulons(adata, tf_names=None, motif_annotations_fname=None, db_fnames=None, thresholds=(0.75, 0.9), top_n_targets=(50,), top_n_regulators=(5, 10, 50), min_genes=20, relation_key=None, key_added=None, copy=False)

Regulon inference for spatial transcriptomics [Aibar17].

Parameters:

adata : AnnData

Annotated data matrix.

tf_names : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

List of transcription factors (TFs).

motif_annotations_fname : str | NoneOptional[str] (default: None)

Path of motif annotation file.

db_fnames : Sequence[str] | NoneOptional[Sequence[str]] (default: None)

List of path of ranking database files.

thresholds

The first method to create the TF-modules based on the best targets for each transcription factor.

top_n_targets

The second method is to select the top targets for a given TF.

top_n_regulators

The alternative way to create the TF-modules is to select the best regulators for each gene.

min_genes

The required minimum number of genes in a resulting module.

relation_key : str | NoneOptional[str] (default: None)

If not specified, it looks .uns[‘relation’] for relational contrastive learning settings (default storage place for run_RCL()). If specified, it looks .uns[relation_key] for relational contrastive learning settings.

key_added : str | NoneOptional[str] (default: None)

If not specified, the regulon inference data is stored in adata.uns[‘regulon’]. If specified, the regulon inference data is added to adata.uns[key_added].

copy : bool (default: False)

Return a copy instead of writing to adata.

Return type:

AnnData | NoneOptional[AnnData]

Returns:

Depending on copy, returns or updates adata with the following fields.

See key_added parameter description for the storage path of the regulon inference.

.uns[‘regulon’]

The inferred regulons.