mowgli.tl#

mowgli.tl.enrich(mdata: MuData, mod: str = 'rna', uns: str = 'H_OT', n_genes: int = 200, sources: Iterable[str] = ['GO:MF', 'GO:CC', 'GO:BP'], ordered: bool = True, domain_scope='custom_annotated')#

Return Gene Set Enrichment Analysis results for each dimension.

Parameters:

  • mdata (md.MuData) – Input data.

  • mod (str, optional) – Modality that contains genes. Defaults to ‘rna’.

  • uns (str, optional) – Name of H matrix. Defaults to ‘H_OT’.

  • n_genes (int, optional) – Number of top genes by dimension. Defaults to 200.

  • sources (Iterable[str], optional) – Enrichment sources. Defaults to [’GO:MF’, ‘GO:CC’, ‘GO:BP’].

  • ordered (bool, optional) – Make query with ordered genes. Defaults to True.

Returns:

Pandas dataframe with the results of the queries, as well as average best p_value across dimensions.

mowgli.tl.top_features(mdata: MuData, mod: str = 'rna', uns: str = 'H_OT', dim: int = 0, threshold: float = 0.2) → Iterable#

Returns the top features for a given modality and latent dimension.

Parameters:

  • mdata (md.MuData) – The input data

  • mod (str, optional) – The modality. Defaults to ‘rna’.

  • uns (str, optional) – Where to look for H. Defaults to ‘H_OT’.

  • dim (int, optional) – The latent dimension. Defaults to 0.

  • n_features (int, optional) – The number of top features. Defaults to 5.

Returns:

A list of features names.

Return type:

Iterable