mowgli.pl#

mowgli.pl.clustermap(mdata: MuData, obsm: str = 'W_OT', cmap='viridis', **kwds)#

Wrapper around Scanpy’s clustermap.

Parameters:

  • mdata (md.MuData) – The input data

  • obsm (str, optional) – The obsm field to consider. Defaults to ‘W_OT’.

  • cmap (str, optional) – The colormap. Defaults to ‘viridis’.

mowgli.pl.enrich(enr: DataFrame, query_name: str, n_terms: int = 10)#

Display a list of enriched terms.

Parameters:

  • enr (pd.DataFrame) – The enrichment object returned by mowgli.tl.enrich()

  • query_name (str) – The name of the query, e.g. “dimension 0”.

mowgli.pl.factor_violin(mdata: MuData, groupby: str, obsm: str = 'W_OT', dim: int = 0, **kwds)#

Make a violin plot of cells for a given latent dimension.

Parameters:

  • mdata (md.MuData) – The input data

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

  • obsm (str, optional) – The embedding. Defaults to ‘W_OT’.

  • groupby (str, optional) – Observation groups.

mowgli.pl.heatmap(mdata: MuData, groupby: str, obsm: str = 'W_OT', cmap: str = 'viridis', sort_var: bool = False, save: str = None, **kwds) → None#

Produce a heatmap of an embedding

Parameters:

  • mdata (md.MuData) – Input data

  • groupby (str) – What to group by

  • obsm (str) – The embedding. Defaults to ‘W_OT’.

  • cmap (str, optional) – Color map. Defaults to ‘viridis’.

  • sort_var (bool, optional) – Sort dimensions by variance. Defaults to False.

mowgli.pl.top_features(mdata: MuData, mod: str = 'rna', uns: str = 'H_OT', dim: int = 0, n_top: int = 10)#

Display the top features for a given dimension.

Parameters:

  • mdata (md.MuData) – The input data

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

  • uns (str, optional) – The uns field to consider. Defaults to ‘H_OT’.

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

  • n_top (int, optional) – The number of top features to display. Defaults to 10.