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 in anndata.AnnData.layers where velocities are stored.

  • xkey (str) – Key in anndata.AnnData.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 from anndata.AnnData.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 for cellrank.tl.kernels.Kernel.

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.

logits

Array of shape (n_cells, n_cells) containing the logits.

params

Parameters which are used to compute the transition matrix.

shape

(n_cells, n_cells).

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[, adata, copy])

Deserialize self from a file.

write(fname[, write_adata, ext])

Serialize self to a file.

write_to_adata([key])

Write the transition matrix and parameters used for computation to the underlying adata object.

Examples

Kernel tricks

Kernel tricks