dynamo.pl.fate_bias(adata, group, basis='umap', fate_bias_df=None, figsize=(6, 4), save_show_or_return='show', save_kwargs={}, **cluster_maps_kwargs)[source]

Plot the lineage (fate) bias of cells states whose vector field trajectories are predicted.

This function internally calls dyn.tl.fate_bias to calculate fate bias dataframe. You can also visualize the data frame via pandas stlying (https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html), for example:

>>> df = dyn.vf.fate_bias(adata)
>>> df.style.background_gradient(cmap='viridis')
  • adata (AnnData) – AnnData object that contains the predicted fate trajectories in the uns attribute.

  • group (str) – The column key that corresponds to the cell type or other group information for quantifying the bias of cell state.

  • basis (str or None (default: None)) – The embedding data space that cell fates were predicted and cell fates will be quantified.

  • fate_bias_df (pandas.DataFrame or None (default: None)) – The DataFrame that stores the fate bias information, calculated via fate_bias_df = dyn.tl.fate_bias(adata).

  • figsize (None or [float, float] (default: None)) – The width and height of a figure.

  • save_show_or_return ({‘show’, ‘save’, ‘return’} (default: show)) – Whether to save, show or return the figure.

  • save_kwargs (dict (default: {})) – A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘fate_bias’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys according to your needs.

  • cluster_maps_kwargs (dict) – Additional arguments passed to sns.clustermap.


Nothing but plot a heatmap shows the fate bias of each cell state to each of the cell group.