cellrank.tl.kernels.ConnectivityKernel

class cellrank.tl.kernels.ConnectivityKernel(adata, backward=False, conn_key='connectivities', compute_cond_num=False, check_connectivity=False)[source]

Kernel which computes transition probabilities based on similarities among cells.

As a measure of similarity, we currently support:

The resulting transition matrix is symmetric and thus cannot be used to learn about the direction of the biological process. To include this direction, consider combining with a velocity-derived transition matrix via cellrank.tl.kernels.VelocityKernel.

Optionally, we apply a density correction as described in [Coifman05], where we use the implementation of [Haghverdi16].

Parameters

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

  • backward (bool) – Direction of the process.

  • conn_key (str) – Key in anndata.AnnData.obsp to obtain the connectivity matrix, describing cell-cell similarity.

  • 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.

  • check_connectivity (bool) – Check whether the underlying KNN graph is connected.

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.

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([density_normalize])

Compute transition matrix based on transcriptomic similarity.

copy()

Return a copy of self.

plot_random_walks(n_sims[, max_iter, seed, …])

Plot random walks in an embedding.

read(fname)

Deserialize self from a file.

write(fname[, ext])

Serialize self to a file.

write_to_adata([key])

Write the transition matrix and parameters used for computation to the underlying adata object.