cellrank.tl.kernels.VelocityKernel¶

class
cellrank.tl.kernels.
VelocityKernel
(adata, backward=False, vkey='velocity', xkey='Ms', gene_subset=None, compute_cond_num=False, check_connectivity=False)[source]¶ Kernel which computes a transition matrix based on velocity correlations.
This borrows ideas from both [Manno18] and [Bergen20]. In short, for each cell i, we compute transition probabilities \(p_{i, j}\) to each cell j in the neighborhood of i. The transition probabilities are computed as a multinomial logistic regression where the weights \(w_j\) (for all j) are given by the vector that connects cell i with cell j in gene expression space, and the features \(x_i\) are given by the velocity vector \(v_i\) of cell i.
 Parameters
adata¶ (
anndata.AnnData
) – Annotated data object.vkey¶ (
str
) – Key inadata
.uns
where the velocities are stored.xkey¶ (
str
) – Key inadata
.layers
where expected gene expression counts are stored.gene_subset¶ (
Optional
[Iterable
]) – List of genes to be used to compute transition probabilities. By default, genes fromadata
.var['velocity_genes']
are used.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
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.
The matrix containing Pearson correlations.
Return rownormalized transition matrix.
Methods
compute_transition_matrix
([mode, …])Compute transition matrix based on velocity directions on the local manifold.
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.