cellrank.external.kernels.WOTKernel.plot_random_walks

WOTKernel.plot_random_walks(n_sims, max_iter=0.25, seed=None, successive_hits=0, start_ixs=None, stop_ixs=None, basis='umap', cmap='gnuplot', linewidth=1.0, linealpha=0.3, ixs_legend_loc=None, n_jobs=None, backend='loky', show_progress_bar=True, figsize=None, dpi=None, save=None, **kwargs)

Plot random walks in an embedding.

This method simulates random walks on the Markov chain defined though the corresponding transition matrix. The method is intended to give qualitative rather than quantitative insights into the transition matrix. Random walks are simulated by iteratively choosing the next cell based on the current cell’s transition probabilities.

Parameters

• n_sims (int) – Number of random walks to simulate.

• max_iter (Union[int, float]) – Maximum number of steps of a random walk. If a float, it can be specified as a fraction of the number of cells.

• seed (Optional[int]) – Random seed.

• successive_hits (int) – Number of successive hits in the stop_ixs required to stop prematurely.

• start_ixs (Union[Sequence[str], Dict[str, Union[str, Sequence[str], Tuple[float, float]]], None]) –

  Cells from which to sample the starting points. If None, use all cells. Can be specified as:

  • dict - dictionary with 1 key in anndata.AnnData.obs with values corresponding to either 1 or more clusters (if the column is categorical) or a tuple specifying [min, max] interval from which to select the indices.

  • typing.Sequence - sequence of cell ids in anndata.AnnData.obs_names.

  For example {'dpt_pseudotime': [0, 0.1]} means that starting points for random walks will be sampled uniformly from cells whose pseudotime is in [0, 0.1].

• stop_ixs (Union[Sequence[str], Dict[str, Union[str, Sequence[str], Tuple[float, float]]], None]) –

  Cells which when hit, the random walk is terminated. If None, terminate after max_iters. Can be specified as:

  • dict - dictionary with 1 key in anndata.AnnData.obs with values corresponding to either 1 or more clusters (if the column is categorical) or a tuple specifying [min, max] interval from which to select the indices.

  • typing.Sequence - sequence of cell ids in anndata.AnnData.obs_names.

  For example {'clusters': ['Alpha', 'Beta']} and successive_hits = 3 means that the random walk will stop prematurely after cells in the above specified clusters have been visited successively 3 times in a row.

• basis (str) – Basis in anndata.AnnData.obsm to use as an embedding.

• cmap (Union[str, LinearSegmentedColormap]) – Colormap for the random walk lines.

• linewidth (float) – Width of the random walk lines.

• linealpha (float) – Alpha value of the random walk lines.

• ixs_legend_loc (Optional[str]) – Legend location for the start/top indices.

• show_progress_bar (bool) – Whether to show a progress bar. Disabling it may slightly improve performance.

• n_jobs (Optional[int]) – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

• backend (str) – Which backend to use for parallelization. See joblib.Parallel for valid options.

• 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.

• kwargs (Any) – Keyword arguments for scvelo.pl.scatter().

Return type

None

Returns

Nothing, just plots the figure. Optionally saves it based on save. For each random walk, the first/last cell is marked by the start/end colors of cmap.