Plot velocity likelihoods as a heatmap. — plot_likelihoods

Visualize the top gene likelihoods for each velocity clusters.

Usage

plot_likelihoods_heatmap(
  adata,
  n_top_genes = 10,
  colorscale = "viridis",
  velocity_clusters = None,
  filename = "velocity_likelihoods.png",
  figsize = None,
  rescale_columns = True,
  return_plot = True
)

Arguments

adata: Anndata object that has been analyzed with python function get_velocity_ligands
n_top_genes: Number of top likelihood genes to visualize. Default 10.
colorscale: Name of a color palette to be passed to seaborn.
velocity_clusters: Character vector of velocity clusters to include in the plot. Default NULL - include all velocity clusters,
filename: Filename to save as.
figsize: Tuple to pass to matplotlib figure(). Default (10,8)
rescale_columns: Boolean. If TRUE, rescales all ligand importances between 0 and 1. This is recommended because random forest importances are relative.
return_plot: Boolean. If TRUE, returns a matplotlib ax object.

Value

a matplotlib ax object, if return_plot is True.