cellrank.tl.kernels.PrecomputedKernel

class cellrank.tl.kernels.PrecomputedKernel(transition_matrix=None, adata=None, backward=False, compute_cond_num=False, **kwargs)

Kernel which contains a precomputed transition matrix.

Parameters

• transition_matrix (Union[ndarray, spmatrix, KernelExpression, str, None]) – Row-normalized transition matrix or a key in anndata.AnnData.obsp. or a cellrank.tl.kernels.KernelExpression with a precomputed transition matrix. If None, try to determine the key based on backward.

• adata (anndata.AnnData) – Annotated data object. If None, a temporary placeholder object is created.

• backward (bool) – Direction of the process.

• compute_cond_num (bool) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.

• kwargs (Any) – Keyword arguments for cellrank.tl.kernels.Kernel.

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(*args, **kwargs)	Return self.
copy()	Return a copy of self.
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.

Examples

Kernel tricks