Source code for

from typing import Union, Callable, Iterable, Optional
from typing_extensions import Literal

from anndata import AnnData
from cellrank import logging as logg
from cellrank.ul._docs import d, inject_docs
from import _deprecate
from import VelocityKernel, ConnectivityKernel
from import KernelExpression
from import BackwardMode, VelocityMode
from import Scheme

[docs]@_deprecate(version="2.0") @inject_docs(m=VelocityMode, b=BackwardMode, s=Scheme) # don't swap the order @d.dedent def transition_matrix( adata: AnnData, backward: bool = False, vkey: str = "velocity", xkey: str = "Ms", conn_key: str = "connectivities", gene_subset: Optional[Iterable] = None, mode: Literal[ "deterministic", "stochastic", "sampling", "monte_carlo" ] = VelocityMode.DETERMINISTIC, backward_mode: Literal["transpose", "negate"] = BackwardMode.TRANSPOSE, scheme: Union[ Literal["dot_product", "cosine", "correlation"], Callable ] = Scheme.CORRELATION, softmax_scale: Optional[float] = None, weight_connectivities: float = 0.2, density_normalize: bool = True, key: Optional[str] = None, **kwargs, ) -> KernelExpression: """ Compute a transition matrix based on a combination of RNA Velocity and transcriptomic or spatial similarity. To learn more about the way in which the transition matrices are computed, see :class:`` for the velocity-based transition matrix and :class:`` for the similarity-based transition matrix. Parameters ---------- %(adata)s %(backward)s vkey Key from ``adata.layers`` to access the velocities. xkey Key in ``adata.layers`` where expected gene expression counts are stored. conn_key Key in :attr:`anndata.AnnData.obsp` to obtain the connectivity matrix, describing cell-cell similarity. gene_subset List of genes to be used to compute transition probabilities. By default, genes from ``adata.var['velocity_genes']`` are used. %(velocity_mode)s %(velocity_backward_mode_high_lvl)s %(velocity_scheme)s %(softmax_scale)s weight_connectivities Weight given to similarities as opposed to velocities. Must be in `[0, 1]`. density_normalize Whether to use density correction when computing the transition probabilities based on similarities. Density correction is done as by :cite:`haghverdi:16`. %(write_to_adata.parameters)s kwargs Keyword arguments for :meth:``. Returns ------- A kernel expression object containing the computed transition matrix. %(write_to_adata)s """ def compute_velocity_kernel() -> VelocityKernel: return VelocityKernel( adata, backward=backward, vkey=vkey, xkey=xkey, gene_subset=gene_subset, conn_key=conn_key, ).compute_transition_matrix( softmax_scale=softmax_scale, mode=mode, backward_mode=backward_mode, scheme=scheme, **kwargs, ) if 0 < weight_connectivities < 1: vk = compute_velocity_kernel()"Using a connectivity kernel with weight `{weight_connectivities}`") ck = ConnectivityKernel( adata, backward=backward, conn_key=conn_key ).compute_transition_matrix(density_normalize=density_normalize) final = ( (1 - weight_connectivities) * vk + weight_connectivities * ck ).compute_transition_matrix() elif weight_connectivities == 0: final = compute_velocity_kernel() elif weight_connectivities == 1: final = ConnectivityKernel( adata, backward=backward, conn_key=conn_key, ).compute_transition_matrix(density_normalize=density_normalize) else: raise ValueError( f"Parameter `weight_connectivities` must be in range `[0, 1]`, found `{weight_connectivities}`." ) final.write_to_adata(key=key) return final