spac.visualization.plot_ripley_l(adata, phenotypes, annotation=None, regions=None, sims=False, return_df=False, **kwargs)[source]

Plot Ripley’s L statistic for multiple bins and different regions for a given pair of phenotypes.

Parameters:

  • adata (AnnData) – AnnData object containing Ripley’s L results in adata.uns[‘ripley_l’].

  • phenotypes (tuple of str) – A tuple of two phenotypes: (center_phenotype, neighbor_phenotype).

  • regions (list of str, optional) – A list of region labels to plot. If None, plot all available regions. Default is None.

  • sims (bool, optional) – Whether to plot the simulation results. Default is False.

  • return_df (bool, optional) – Whether to return the DataFrame containing the Ripley’s L results.

  • kwargs (dict, optional) – Additional keyword arguments to pass to seaborn.lineplot.

Raises:

ValueError – If the Ripley L results are not found in adata.uns[‘ripley_l’].

Returns:

  • ax (matplotlib.axes.Axes) – The Axes object containing the plot, which can be further modified.

  • df (pandas.DataFrame, optional) – The DataFrame containing the Ripley’s L results, if return_df is True.

Example

>>> ax = plot_ripley_l(
...     adata,
...     phenotypes=('Phenotype1', 'Phenotype2'),
...     regions=['region1', 'region2'])
>>> plt.show()

This returns the Axes object for further customization and displays the plot.