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, **kwargs)[source]¶
Kernel which computes a transition matrix based on RNA velocity.
This borrows ideas from both [La Manno et al., 2018] and [Bergen et al., 2020]. 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.backward (
bool
) – Direction of the process.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.kwargs (
Any
) – Keyword arguments forcellrank.tl.kernels.Kernel
.
Attributes
Annotated data object.
Direction of the process.
Condition number of the transition matrix.
Get the kernels of the kernel expression, except for constants.
Array of shape
(n_cells, n_cells)
containing the logits.Parameters which are used to compute the 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
([mode, …])Compute transition matrix based on velocity directions on the local manifold.
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)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.