# 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 and . 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. 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. 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. 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 . 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.