# cellrank.tl.kernels.ConnectivityKernel¶

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

Kernel which computes transition probabilities based on transcriptomic similarities.

As a measure for transcriptomic similarity, we use the weighted KNN graph computed using scanpy.pp.neighbors(), see [Wolf18]. By definition, the resulting transition matrix is symmetric and cannot be used to learn about the direction of the developmental process under consideration. However, the velocity-derived transition matrix from cellrank.tl.kernels.VelocityKernel can be combined with the similarity-based transition matrix as a means of regularization.

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

Parameters

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_transition_matrix([density_normalize]) Compute transition matrix based on transcriptomic similarity. Return a copy of self. 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.