liana.pl.misty_contributions

liana.pl.misty_contributions(misty=None, target_metrics=None, view_names=None, filter_fn=None, aggregate_fn=None, figure_size=(5, 5), return_fig=True)

Plot view contributions per target.

Parameters:

misty MistyData | None (default: None)

MistyData object with modelling results

target_metrics DataFrame | None (default: None)

A target_metrics DataFrame

view_names list[str] | None (default: None)

A list of view names to plot

filter_fn Callable[[Series], bool] | None (default: None)

A function, applied along the columns (axis=1) of the results, used to filter the results to be plotted. In dotplot and tileplot it selects interactions rather than rows: an interaction (ligand_complex -> receptor_complex) is kept whenever the predicate holds for any of its source-target pairs, and is then plotted for all of them, so rows that fail the predicate are drawn alongside those that pass. circle and the misty plots filter row-wise.

aggregate_fn Callable[[Series], object] | str | None (default: None)

A function used to aggregate the results to be plotted.

figure_size tuple[float, float] (default: (5, 5))

Figure x,y size

return_fig bool (default: True)

bool whether to return the fig object.

Return type:

plotnine.ggplot | None

Returns:

A plotnine plot.

Raises:

ValueError – If the misty input is not valid or no view_names provided.

Examples

Stacks each view’s contribution to every target into one bar, read from misty.uns['target_metrics']:

>>> import liana as li
>>> adata = li.ds.generate_toy_spatial()
>>> adata = adata[:, adata.var_names[:5]].copy()
>>> misty = li.mt.genericMistyData(intra=adata, bandwidth=200, set_diag=True)
>>> misty(model=li.mt.sp.LinearModel)
>>> p = li.pl.misty_contributions(misty)