Source code for

from anndata import AnnData
from cellrank import logging as logg
from cellrank.ul._docs import d
from import ConnectivityMixin
from import UnidirectionalKernel

__all__ = ("ConnectivityKernel",)

[docs]@d.dedent class ConnectivityKernel(ConnectivityMixin, UnidirectionalKernel): """ Kernel which computes transition probabilities based on similarities among cells. As a measure of similarity, we currently support: - transcriptomic similarities, computed using e.g. :func:`scanpy.pp.neighbors`, see :cite:`wolf:18`. - spatial similarities, computed using e.g. :func:``, see :cite:`palla:21`. The resulting transition matrix is symmetric and thus cannot be used to learn about the direction of the biological process. To include this direction, consider combining with a velocity-derived transition matrix via :class:`cellrank.kernels.VelocityKernel`. %(density_correction)s Parameters ---------- %(adata)s conn_key Key in :attr:`anndata.AnnData.obsp` where connectivity matrix describing cell-cell similarity is stored. check_connectivity Check whether the underlying kNN graph is connected. """ def __init__( self, adata: AnnData, conn_key: str = "connectivities", check_connectivity: bool = False, ): super().__init__( adata, conn_key=conn_key, check_connectivity=check_connectivity, )
[docs] def compute_transition_matrix( self, density_normalize: bool = True ) -> "ConnectivityKernel": """ Compute transition matrix based on transcriptomic similarity. Uses symmetric, weighted kNN graph to compute symmetric transition matrix. The connectivities are computed using :func:`scanpy.pp.neighbors`. Depending on the parameters used there, they can be UMAP connectivities or gaussian-kernel-based connectivities with adaptive kernel width. Parameters ---------- density_normalize Whether or not to use the underlying kNN graph for density normalization. Returns ------- Self and updates :attr:`transition_matrix` and :attr:`params`. """ # fmt: off start ="Computing transition matrix based on `adata.obsp[{self._conn_key!r}]`") if self._reuse_cache({"dnorm": density_normalize, "key": self._conn_key}, time=start): return self self.transition_matrix = self._density_normalize(self._conn) if density_normalize else self._conn" Finish", time=start) # fmt: on return self