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 velocityderived transition matrix fromcellrank.tl.kernels.VelocityKernel
can be combined with the similaritybased 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
Annotated data object.
Direction of the process.
Condition number of the transition matrix.
Get the kernels of the kernel expression, except for constants.
Parameters which are used to compute the transition matrix.
Return rownormalized transition matrix.
Methods
compute_transition_matrix
([density_normalize])Compute transition matrix based on transcriptomic similarity.
copy
()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.