# cellrank.tl.kernels.ConnectivityKernel.compute_transition_matrix¶

ConnectivityKernel.compute_transition_matrix(density_normalize=True)[source]

Compute transition matrix based on transcriptomic similarity.

Uses symmetric, weighted KNN graph to compute symmetric transition matrix. The connectivities are computed using scanpy.pp.neighbors(). Depending on the parameters used there, they can be UMAP connectivities or gaussian-kernel-based connectivities with adaptive kernel width.

Parameters

density_normalize (bool) – Whether or not to use the underlying KNN graph for density normalization.

Returns

Makes transition_matrix available.

Return type

cellrank.tl.kernels.ConnectivityKernel