liana.plotting.feature_by_group

liana.plotting.feature_by_group#

liana.plotting.feature_by_group(adata=None, groupby=None, spatial_key='spatial', labels=None, feature=None, figure_size=(10, 6), normalize=True, percentile_scaling=None, show_counts=True, **kwargs)#

Plot inflow scores for single feature across spatial coordinates.

Parameters:

  • adata (AnnData (default: None)) – Annotated data object.

  • groupby (str (default: None)) – Key to be used for grouping.

  • spatial_key (default: 'spatial') – Key in adata.obsm that contains the spatial coordinates. Default is 'spatial'.

  • labels (list[str] (default: None)) – List of labels to compare, from groupby.

  • feature (str (default: None)) – From adata.var_names.

  • figure_size (tuple (default: (10, 6))) – Figure x,y size

  • normalize (bool (default: True)) – Normalize expression values between 0 and 1 for each cell type.

  • percentile_scaling (tuple[int, int] | None (default: None)) – Tuple specifying percentiles for scaling.

  • show_counts (bool (default: True)) – Show counts of expression cells (expression > 0).

Returns:

A tuple of the matplotlib Figure and its main Axes.

Examples

adata is typically the output of liana.method.inflow, whose var_names are 'source^ligand^receptor' triplets. Each label in labels gets its own colormap and colorbar, so the groups can be compared in place:

>>> import liana as li
>>> adata = li.testing.generate_toy_spatial()
>>> lrdata = li.mt.inflow(adata, groupby='bulk_labels',
...                       resource_name='consensus')
>>> fig, ax = li.pl.feature_by_group(lrdata,
...                                  groupby='bulk_labels',
...                                  labels=['Dendritic', 'CD14+ Monocyte'],
...                                  feature=lrdata.var_names[0])