stereo.algorithm.regulatory_network_inference.PlotRegulatoryNetwork.grn_dotplot#

PlotRegulatoryNetwork.grn_dotplot(cluster_res_key, regulon_names=None, celltypes=None, groupby='group', cell_label='bins', network_res_key='regulatory_network_inference', palette='Reds', width=None, height=None, **kwargs)[source]#

Intuitive way of visualizing how feature expression changes across different identity classes (clusters). The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level across all cells within a class (red is high).

Parameters:

  • cluster_res_key (str) – the key which specifies the clustering result in data.tl.result.

  • regulon_names (Union[str, list, None]) – the regulon which would be shown on plot, defaults to None. If set it to None, it will be set to all regulon. 1) string: only one cluster. 2) list: an array contains the regulon which would be shown.

  • celltypes (Union[str, list, None]) – the celltypes in cluster pairs which would be shown on plot, defaults to None. If set it to None, it will be set to all clusters. 1) string: only one cluster. 2) list: an array contains the clusters which would be shown.

  • groupby (str) – cell type label.

  • cell_label (str) – cell bin label.

  • network_res_key (str) – the key which specifies inference regulatory network result in data.tl.result, defaults to ‘regulatory_network_inference’

  • palette (str) – Color theme, defaults to ‘Reds’

  • kwargs – features Input vector of features, or named list of feature vectors

  • width (Optional[int]) – the figure width in pixels.

  • height (Optional[int]) – the figure height in pixels.

Returns:

matplotlib.figure