cellrank.external.kernels.StationaryOTKernel
- class cellrank.external.kernels.StationaryOTKernel(adata, g, terminal_states=None, **kwargs)[source]
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 inanndata.AnnData.obs
or a categoricalpandas.Series
where non-NaN values mark terminal states. If None, terminal states are assumed to be present inanndata.AnnData.obs
['terminal_states']
.g (
Union
[str
,ndarray
]) – Key inanndata.AnnData.obs
containing relative growth rates for cells or the array itself.kwargs (
Any
) – Additional keyword arguments.
Attributes
Annotated data object.
Direction of the process.
Condition number of the transition matrix.
Get the kernels of the kernel expression, except for constants.
Parameters which are used to compute the transition matrix.
(n_cells, n_cells).
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.