cellrank.tl.kernels.PrecomputedKernel
- class cellrank.tl.kernels.PrecomputedKernel(object, adata=None, obsp_key=None, **kwargs)[source]
Kernel which contains a precomputed transition matrix.
- Parameters
object (
Union
[str
,bool
,ndarray
,spmatrix
,AnnData
,KernelExpression
]) –Can be one of the following types:
anndata.AnnData
- annotated data object.scipy.sparse.spmatrix
,numpy.ndarray
- row-normalized transition matrix.cellrank.tl.kernels.KernelExpression
- kernel expression.str
- key inanndata.AnnData.obsp
where the transition matrix is stored.adata
must be provided in this case.bool
- directionality of the transition matrix that will be used to infer its storage location. If None, the directionality will be determined automatically.adata
must be provided in this case.
adata (
anndata.AnnData
) – Annotated data object. Must be provided whenobject
isstr
orbool
.obsp_key (
Optional
[str
]) – Key inanndata.AnnData.obsp
where the transition matrix is stored. If None, it will be determined automatically. Only used whenobject
isanndata.AnnData
.copy – Whether or not to copy the stored transition matrix.
backward – Hint whether this is a forward, backward or a unidirectional kernel. Only used when
object
isanndata.AnnData
.
Attributes
Annotated data object.
Direction of the process.
Underlying base kernels.
Parameters which are used to compute the transition matrix.
(n_cells, n_cells)
.Row-normalized transition matrix.
Methods
compute_transition_matrix
(*_, **__)Do nothing and return self.
copy
(*[, deep])Return a copy of itself.
plot_projection
([basis, key_added, ...])Plot
transition_matrix
as a stream or a grid plot.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, copy])Write the transition matrix and parameters used for computation to the underlying
adata
object.