Source code for cellrank.tl.kernels._connectivity_kernel

from copy import copy

from anndata import AnnData
from cellrank import logging as logg
from cellrank.ul._docs import d
from cellrank.tl.kernels import Kernel


[docs]@d.dedent class ConnectivityKernel(Kernel): """ 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:`squidpy.gr.spatial_neighbors`, 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.tl.kernels.VelocityKernel`. %(density_correction)s Parameters ---------- %(adata)s %(backward)s conn_key Key in :attr:`anndata.AnnData.obsp` to obtain the connectivity matrix describing cell-cell similarity. %(cond_num)s check_connectivity Check whether the underlying KNN graph is connected. """ def __init__( self, adata: AnnData, backward: bool = False, conn_key: str = "connectivities", compute_cond_num: bool = False, check_connectivity: bool = False, ): super().__init__( adata, backward=backward, compute_cond_num=compute_cond_num, check_connectivity=check_connectivity, conn_key=conn_key, ) self._key = conn_key
[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 updated :attr:`transition_matrix`. """ # fmt: off start = logg.info(f"Computing transition matrix based on `adata.obsp[{self._key!r}]`") if self._reuse_cache({"dnorm": density_normalize, "key": self._key}, time=start): return self self._compute_transition_matrix(matrix=self._conn.copy(), density_normalize=density_normalize) logg.info(" Finish", time=start) # fmt: on return self
[docs] def copy(self) -> "ConnectivityKernel": """Return a copy of self.""" ck = ConnectivityKernel(self.adata, backward=self.backward) ck._params = copy(self.params) ck._cond_num = self.condition_number ck._transition_matrix = copy(self._transition_matrix) return ck