cellrank.external.kernels.StationaryOTKernel

class cellrank.external.kernels.StationaryOTKernel(adata, g, terminal_states=None, **kwargs)

Stationary optimal transport kernel from [Zhang et al., 2021].

This class requires the statOT package, which can be installed as pip install statot POT.

Parameters

• adata (anndata.AnnData) – Annotated data object.

• terminal_states (Union[str, Series, None]) – Key in anndata.AnnData.obs or a categorical pandas.Series where non-NaN values mark terminal states. If None, terminal states are assumed to be present in anndata.AnnData.obs ['terminal_states'].

• g (Union[str, ndarray]) – Key in anndata.AnnData.obs containing relative growth rates for cells or the array itself.

• kwargs (Any) – Additional keyword arguments.

Attributes

adata	Annotated data object.
backward	Direction of the process.
condition_number	Condition number of the transition matrix.
kernels	Get the kernels of the kernel expression, except for constants.
params	Parameters which are used to compute the transition matrix.
shape	(n_cells, n_cells).
transition_matrix	Return row-normalized transition matrix.

Methods

compute_projection([basis, key_added, copy])	Compute a projection of the transition matrix in the embedding.
compute_transition_matrix(eps, dt[, basis, ...])	Compute transition matrix using stationary OT [Zhang et al., 2021].
copy()	Return a copy of itself.
plot_random_walks(n_sims[, max_iter, seed, ...])	Plot random walks in an embedding.
plot_single_flow(cluster, cluster_key, time_key)	Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].
read(fname[, adata, copy])	Deserialize self from a file.
write(fname[, write_adata, ext])	Serialize self to a file.
write_to_adata([key])	Write the transition matrix and parameters used for computation to the underlying adata object.