- class cellrank.tl.kernels.PseudotimeKernel(adata, backward=False, time_key='dpt_pseudotime', compute_cond_num=False, check_connectivity=False, **kwargs)¶
Kernel which computes directed transition probabilities based on a KNN graph and pseudotime.
The KNN graph contains information about the (undirected) connectivities among cells, reflecting their similarity. Pseudotime can be used to either remove edges that point against the direction of increasing pseudotime [Setty et al., 2019], or to downweight them [Stassen et al., 2021].
anndata.AnnData) – Annotated data object.
bool) – Direction of the process.
bool) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.
Annotated data object.
Direction of the process.
Condition number of the transition matrix.
Get the kernels of the kernel expression, except for constants.
Parameters which are used to compute the transition matrix.
Pseudotemporal ordering of cells.
Return row-normalized transition matrix.
compute_projection([basis, key_added, copy])
Compute a projection of the transition matrix in the embedding.
Compute transition matrix based on KNN graph and pseudotemporal ordering.
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 the transition matrix and parameters used for computation to the underlying