cellrank.tl.kernels.VelocityKernel

class cellrank.tl.kernels.VelocityKernel(adata, backward=False, xkey='Ms', vkey='velocity', **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

Attributes

adata

Annotated data object.

backward

Direction of the process.

kernels

Underlying base kernels.

logits

Array of shape (n_cells, n_cells) containing unnormalized transition matrix.

params

Parameters which are used to compute the transition matrix.

shape

(n_cells, n_cells).

transition_matrix

Row-normalized transition matrix.

Methods

compute_transition_matrix([model, ...])

Compute transition matrix based on velocity directions on the local manifold.

copy(*[, deep])

Return a copy of itself.

plot_projection([basis, key_added, ...])

Plot transition_matrix as a stream or a grid plot.

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, copy])

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