pyVIA.plotting_via.plot_differentiation_flow

pyVIA.plotting_via.plot_differentiation_flow(via_object, idx=None, dpi=150, marker_lineages=[], label_node=[], do_log_flow=True, fontsize=8, alpha_factor=0.9, majority_cluster_population_dict=None, cmap_sankey='rainbow', title_str='Differentiation Flow', root_cluster_list=None)[source]

#SANKEY PLOTS G is the igraph knn (low K) used for shortest path in high dim space. no idx needed as it’s made on full sample knn_hnsw is the knn made in the embedded space used for query to find the nearest point in the downsampled embedding that corresponds to the single cells in the full graph

Parameters:

  • via_object –

  • embedding – n_samples x 2. embedding is 2D representation of the full dataset.

  • idx (list) – if one uses a downsampled embedding of the original data, then idx is the selected indices of the downsampled samples used in the visualization

  • cmap_name –

  • dpi –

:param do_log_flow bool True (default) take the natural log (1+edge flow value) :param label_node list of labels for each cell (could be cell type, stage level) length is n_cells :param scatter_size: if None, then auto determined based on n_cells :param marker_lineages: Default is to use all lineage pathways. other provide a list of lineage number (terminal cluster number). :type alpha_factor: float :param alpha_factor: float transparency :type root_cluster_list: list :param root_cluster_list: list of roots by cluster number e.g. [5] means a good root is cluster number 5 :return: fig, axs