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:

transcriptomic similarities, computed using e.g. scanpy.pp.neighbors(), see [Wolf et al., 2018].

spatial similarities, computed using e.g. squidpy.gr.spatial_neighbors(), see [Palla et al., 2021].

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 [Coifman et al., 2005], where we use the implementation of [Haghverdi et al., 2016].

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.
`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`([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.
`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