# 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)[source]

Kernel which computes a transition matrix based on velocity correlations.

This borrows ideas from both [Manno18] and [Bergen20]. 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. params Parameters which are used to compute the transition matrix. pearson_correlations The matrix containing Pearson correlations. transition_matrix Return row-normalized transition matrix.

Methods

 compute_transition_matrix([mode, …]) Compute transition matrix based on velocity directions on the local manifold. Return a copy of self. 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.