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

adata (anndata.AnnData) – Annotated data object.
backward (bool) – Direction of the process.
xkey (Optional[str]) – Key in anndata.AnnData.layers where expected gene expression counts are stored.
vkey (Optional[str]) – Key in anndata.AnnData.layers where velocities are stored.
gene_subset – List of genes to be used to compute transition probabilities. If not specified, genes from anndata.AnnData.var ['{vkey}_genes'] are used.
kwargs (Any) – Keyword arguments for the parent class.

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.