# -*- coding: utf-8 -*-
"""Connectivity kernel module."""
from copy import copy
from cellrank import logging as logg
from cellrank.ul._docs import d
from cellrank.tl.kernels import Kernel
from cellrank.tl.kernels._base_kernel import (
_LOG_USING_CACHE,
_ERROR_EMPTY_CACHE_MSG,
AnnData,
)
[docs]@d.dedent
class ConnectivityKernel(Kernel):
"""
Kernel which computes transition probabilities based on transcriptomic similarities.
As a measure for transcriptomic similarity, we use the weighted KNN graph computed using
:func:`scanpy.pp.neighbors`, see [Wolf18]_.
By definition, the resulting transition matrix is symmetric and cannot be used to learn about the direction of the
developmental process under consideration.
However, the velocity-derived transition matrix from :class:`cellrank.tl.kernels.VelocityKernel` can be combined
with the similarity-based transition matrix as a means of regularization.
%(density_correction)s
Parameters
----------
%(adata)s
%(backward)s
compute_cond_num
Whether to compute condition number of the transition matrix. Note that this might be costly,
since it does not use sparse implementation.
check_connectivity
Check whether the underlying KNN graph is connected.
"""
def __init__(
self,
adata: AnnData,
backward: bool = False,
compute_cond_num: bool = False,
check_connectivity: bool = False,
):
super().__init__(
adata,
backward=backward,
compute_cond_num=compute_cond_num,
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
-------
:class:`cellrank.tl.kernels.ConnectivityKernel`
Makes :paramref:`transition_matrix` available.
"""
start = logg.info("Computing transition matrix based on connectivities")
params = {"dnorm": density_normalize}
if params == self.params:
assert self.transition_matrix is not None, _ERROR_EMPTY_CACHE_MSG
logg.debug(_LOG_USING_CACHE)
logg.info(" Finish", time=start)
return self
self._params = params
self._compute_transition_matrix(
matrix=self._conn.copy(), density_normalize=density_normalize
)
logg.info(" Finish", time=start)
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