cellrank.tl.kernels.PrecomputedKernel.plot_single_flow

PrecomputedKernel.plot_single_flow(cluster, cluster_key, time_key, clusters=None, time_points=None, min_flow=0, remove_empty_clusters=True, ascending=False, legend_loc='upper right out', alpha=0.8, xticks_step_size=1, figsize=None, dpi=None, save=None, show=True)

Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].

Parameters
  • cluster (str) – Cluster for which to visualize outgoing flow.

  • cluster_key (str) – Key in anndata.AnnData.obs where clustering is stored.

  • time_key (str) – Key in anndata.AnnData.obs where experimental time is stored.

  • clusters (Optional[Sequence[Any]]) – Visualize flow only for these clusters. If None, use all clusters.

  • time_points (Optional[Sequence[Union[float, int]]]) – Visualize flow only for these time points. If None, use all time points.

  • min_flow (float) – Only show flow edges with flow greater than this value. Flow values are always in [0, 1].

  • remove_empty_clusters (bool) – Whether to remove clusters with no incoming flow edges.

  • ascending (Optional[bool]) – Whether to sort the cluster by ascending or descending incoming flow. If None, use the order as in defined by clusters.

  • alpha (Optional[float]) – Alpha value for cell proportions.

  • xticks_step_size (Optional[int]) – Show only every n-th ticks on x-axis. If None, don’t show any ticks.

  • legend_loc (Optional[str]) – Position of the legend. If None, do not show the legend.

  • figsize (Optional[Tuple[float, float]]) – Size of the figure.

  • dpi (Optional[int]) – Dots per inch.

  • save (Union[str, Path, None]) – Filename where to save the plot.

  • show (bool) – If False, return matplotlib.pyplot.Axes.

Return type

Optional[Axes]

Returns

The axes object, if show = False. Nothing, just plots the figure. Optionally saves it based on save.

Notes

This function is a Python reimplementation of the following original R function with some minor stylistic differences. This function will not recreate the results from [Mittnenzweig et al., 2021], because there, the Metacell model [Baran et al., 2019] was used to compute the flow, whereas here the transition matrix is used.