Source code for cellrank.kernels._pseudotime_kernel

import enum
from typing import Any, Callable, Literal, Optional, Union

import numpy as np

from anndata import AnnData

from cellrank import logging as logg
from cellrank._utils._docs import d
from cellrank._utils._enum import DEFAULT_BACKEND, Backend_t, ModeEnum
from cellrank._utils._utils import _connected, _irreducible
from cellrank.kernels._base_kernel import BidirectionalKernel
from cellrank.kernels.mixins import ConnectivityMixin
from cellrank.kernels.utils._pseudotime_scheme import (

__all__ = ["PseudotimeKernel"]

class ThresholdScheme(ModeEnum):
    SOFT =
    HARD =

[docs] @d.dedent class PseudotimeKernel(ConnectivityMixin, BidirectionalKernel): """Kernel which computes directed transition probabilities based on a k-NN graph and pseudotime. .. seealso:: - See :doc:`../../../notebooks/tutorials/kernels/300_pseudotime` on how to compute the :attr:`~cellrank.kernels.PseudotimeKernel.transition_matrix` based on the pseudotime. The k-NN 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 :cite:`setty:19` or to down-weight them :cite:`stassen:21`. Parameters ---------- %(adata)s %(backward)s If :obj:`True`, the :attr:`pseudotime` will be set to ``max(pseudotime) - pseudotime``. time_key Key in :attr:`~anndata.AnnData.obs` where the pseudotime is stored. kwargs Keyword arguments for the :class:`~cellrank.kernels.ConnectivityKernel`. """ def __init__( self, adata: AnnData, time_key: str, backward: bool = False, **kwargs: Any, ): super().__init__( adata, backward=backward, time_key=time_key, **kwargs, ) def _read_from_adata(self, time_key: str, **kwargs: Any) -> None: super()._read_from_adata(**kwargs) # fmt: off self._time_key = time_key if time_key not in self.adata.obs: raise KeyError(f"Unable to find pseudotime in `adata.obs[{time_key!r}]`.") self._pseudotime = np.array(self.adata.obs[time_key]).astype(np.float64, copy=True) if np.any(np.isnan(self._pseudotime)): raise ValueError("Encountered NaN values in pseudotime.") # fmt: on
[docs] @d.dedent def compute_transition_matrix( self, threshold_scheme: Union[ Literal["soft", "hard"], Callable[[float, np.ndarray, np.ndarray], np.ndarray], ] = "hard", frac_to_keep: float = 0.3, b: float = 10.0, nu: float = 0.5, check_irreducibility: bool = False, n_jobs: Optional[int] = None, backend: Backend_t = DEFAULT_BACKEND, show_progress_bar: bool = True, **kwargs: Any, ) -> "PseudotimeKernel": """Compute transition matrix based on k-NN graph and pseudotemporal ordering. Depending on the choice of the ``threshold_scheme``, it is based on ideas by either *Palantir* :cite:`setty:19` or *VIA* :cite:`stassen:21`. Parameters ---------- threshold_scheme Which method to use when biasing the graph. Valid options are: - ``'hard'`` - based on *Palantir* :cite:`setty:19` which removes some edges that point against the direction of increasing pseudotime. To avoid disconnecting the graph, it does not remove all edges that point against the direction of increasing pseudotime, but keeps the ones that point to cells inside a close radius. This radius is chosen according to the local cell density. - ``'soft'`` - based on *VIA* :cite:`stassen:21` which down-weights edges that points against the direction of increasing pseudotime. Essentially, the further "behind" a query cell is in pseudotime with respect to the current reference cell, the more penalized will be its graph-connectivity. - :class:`callable` - any function conforming to the signature of :func:`cellrank.kernels.utils.ThresholdSchemeABC.__call__`. frac_to_keep Fraction of the closest neighbors (according to graph connectivities) are kept, no matter whether they lie in the pseudotemporal past or future. This is done to ensure that the graph remains connected. Only used when ``threshold_scheme = 'hard'``. Must be in :math:`[0, 1]`. %(soft_scheme_kernel)s check_irreducibility Optional check for irreducibility of the final transition matrix. %(parallel)s kwargs Keyword arguments for ``threshold_scheme``. Returns ------- Returns self and updates :attr:`transition_matrix` and :attr:`params`. """ if self.pseudotime is None: raise ValueError("Compute pseudotime first.") # CytoTraceKernel start ="Computing transition matrix based on pseudotime") if isinstance(threshold_scheme, str): threshold_scheme = ThresholdScheme(threshold_scheme) if threshold_scheme == ThresholdScheme.SOFT: scheme = SoftThresholdScheme() kwargs["b"] = b kwargs["nu"] = nu elif threshold_scheme == ThresholdScheme.HARD: scheme = HardThresholdScheme() kwargs["frac_to_keep"] = frac_to_keep else: raise NotImplementedError(f"Threshold scheme `{threshold_scheme}` is not yet implemented.") elif isinstance(threshold_scheme, ThresholdSchemeABC): scheme = threshold_scheme elif callable(threshold_scheme): scheme = CustomThresholdScheme(threshold_scheme) else: raise TypeError( f"Expected `threshold_scheme` to be either a `str` or a `callable`, found `{type(threshold_scheme)}`." ) # fmt: off if self._reuse_cache({"dnorm": False, "scheme": str(threshold_scheme), **kwargs}, time=start): return self # fmt: on biased_conn = scheme.bias_knn( self.connectivities, self.pseudotime, n_jobs=n_jobs, backend=backend, show_progress_bar=show_progress_bar, **kwargs, ) # make sure the biased graph is still connected if not _connected(biased_conn): logg.warning("Biased k-NN graph is disconnected") if check_irreducibility and not _irreducible(biased_conn): logg.warning("Biased k-NN graph is not irreducible") self.transition_matrix = biased_conn" Finish", time=start) return self
@property def pseudotime(self) -> Optional[np.array]: """Pseudotemporal ordering of cells.""" if self._pseudotime is None: return None if self.backward: return np.max(self._pseudotime) - self._pseudotime return self._pseudotime def __invert__(self) -> "PseudotimeKernel": pk = self._copy_ignore("_transition_matrix") pk._backward = not self.backward pk._params = {} return pk