import types
from typing import Any, Literal, Mapping, Optional, Tuple, Type, Union
from anndata import AnnData
from moscot import _constants
from moscot._types import (
CostFnMap_t,
CostKwargs_t,
Numeric_t,
OttCostFn_t,
Policy_t,
ProblemStage_t,
QuadInitializer_t,
ScaleCost_t,
SinkhornInitializer_t,
)
from moscot.base.problems.birth_death import BirthDeathMixin, BirthDeathProblem
from moscot.base.problems.compound_problem import B, CompoundProblem
from moscot.problems._utils import handle_cost, handle_joint_attr
from moscot.problems.time._mixins import TemporalMixin
__all__ = ["TemporalProblem", "LineageProblem"]
[docs]
class TemporalProblem( # type: ignore[misc]
TemporalMixin[Numeric_t, BirthDeathProblem], BirthDeathMixin, CompoundProblem[Numeric_t, BirthDeathProblem]
):
"""Class for analyzing time-series single cell data based on :cite:`schiebinger:19`.
Parameters
----------
adata
Annotated data object.
kwargs
Keyword arguments for :class:`~moscot.base.problems.CompoundProblem`.
"""
def __init__(self, adata: AnnData, **kwargs: Any):
super().__init__(adata, **kwargs)
[docs]
def prepare(
self,
time_key: str,
joint_attr: Optional[Union[str, Mapping[str, Any]]] = None,
policy: Literal["sequential", "triu", "tril", "explicit"] = "sequential",
cost: OttCostFn_t = "sq_euclidean",
cost_kwargs: CostKwargs_t = types.MappingProxyType({}),
a: Optional[Union[bool, str]] = None,
b: Optional[Union[bool, str]] = None,
marginal_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
**kwargs: Any,
) -> "TemporalProblem":
"""Prepare the temporal problem problem.
.. seealso::
- See :doc:`../notebooks/tutorials/200_temporal_problem` on how to
prepare and solve the :class:`~moscot.problems.time.TemporalProblem`.
Parameters
----------
time_key
Key in :attr:`~anndata.AnnData.obs` where the time points are stored.
joint_attr
How to get the data that defines the :term:`linear problem`:
- :obj:`None` - `PCA <https://en.wikipedia.org/wiki/Principal_component_analysis>`_
on :attr:`~anndata.AnnData.X` is computed.
- :class:`str` - key in :attr:`~anndata.AnnData.obsm` where the data is stored.
- :class:`dict` - it should contain ``'attr'`` and ``'key'``, the attribute and key in
:class:`~anndata.AnnData`, and optionally ``'tag'`` from the
:class:`tags <moscot.utils.tagged_array.Tag>`.
By default, :attr:`tag = 'point_cloud' <moscot.utils.tagged_array.Tag.POINT_CLOUD>` is used.
policy
Rule which defines how to construct the subproblems using :attr:`obs['{time_key}'] <anndata.AnnData.obs>`.
Valid options are:
- ``'sequential'`` - align subsequent time points ``[(t0, t1), (t1, t2), ...]``.
- ``'triu'`` - upper triangular matrix ``[(t0, t1), (t0, t2), ..., (t1, t2), ...]``.
- ``'tril'`` - lower triangular matrix ``[(t_n, t_n-1), (t_n, t0), ..., (t_n-1, t_n-2), ...]``.
- ``'explicit'`` - explicit sequence of subsets passed via ``subset = [(b3, b0), ...]``.
cost
Cost function to use. Valid options are:
- :class:`str` - name of the cost function, see :func:`~moscot.costs.get_available_costs`.
- :class:`dict` - a dictionary with the following keys and values:
- ``'xy'`` - cost function for the :term:`linear term`, same as above.
cost_kwargs
Keyword arguments for the :class:`~moscot.base.cost.BaseCost` or any backend-specific cost.
a
Source :term:`marginals`. Valid options are:
- :class:`str` - key in :attr:`~anndata.AnnData.obs` where the source marginals are stored.
- :class:`bool` - if :obj:`True`,
:meth:`estimate the marginals <moscot.base.problems.BirthDeathProblem.estimate_marginals>`,
otherwise use uniform marginals.
- :obj:`None` - set to :obj:`True` if :attr:`proliferation_key` or :attr:`apoptosis_key` is not :obj:`None`.
b
Target :term:`marginals`. Valid options are:
- :class:`str` - key in :attr:`~anndata.AnnData.obs` where the target marginals are stored.
- :class:`bool` - if :obj:`True`,
:meth:`estimate the marginals <moscot.base.problems.BirthDeathProblem.estimate_marginals>`,
otherwise use uniform marginals.
- :obj:`None` - set to :obj:`True` if :attr:`proliferation_key` or :attr:`apoptosis_key` is not :obj:`None`.
marginal_kwargs
Keyword arguments for :meth:`~moscot.base.problems.BirthDeathProblem.estimate_marginals`.
It always contains :attr:`proliferation_key` and :attr:`apoptosis_key`,
see :meth:`score_genes_for_marginals` for more information.
kwargs
Keyword arguments for :meth:`~moscot.base.problems.CompoundProblem.prepare`.
Returns
-------
Returns self and updates the following fields:
- :attr:`problems` - the prepared subproblems.
- :attr:`solutions` - set to an empty :class:`dict`.
- :attr:`temporal_key` - key in :attr:`~anndata.AnnData.obs` where time points are stored.
- :attr:`stage` - set to ``'prepared'``.
- :attr:`problem_kind` - set to ``'linear'``.
"""
self.temporal_key = time_key
xy, kwargs = handle_joint_attr(joint_attr, kwargs)
xy, x, y = handle_cost(
xy=xy, x=kwargs.pop("x", {}), y=kwargs.pop("y", {}), cost=cost, cost_kwargs=cost_kwargs, **kwargs
)
marginal_kwargs = dict(marginal_kwargs)
estimate_marginals = self.proliferation_key is not None or self.apoptosis_key is not None
a = estimate_marginals if a is None else a
b = estimate_marginals if b is None else b
return super().prepare( # type: ignore[return-value]
key=time_key,
xy=xy,
x=x,
y=y,
policy=policy,
cost=None, # cost information is already stored in x,y,xy
marginal_kwargs=marginal_kwargs,
a=a,
b=b,
**kwargs,
)
[docs]
def solve(
self,
epsilon: float = 1e-3,
tau_a: float = 1.0,
tau_b: float = 1.0,
rank: int = -1,
scale_cost: ScaleCost_t = "mean",
batch_size: Optional[int] = None,
stage: Union[ProblemStage_t, Tuple[ProblemStage_t, ...]] = ("prepared", "solved"),
initializer: SinkhornInitializer_t = None,
initializer_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
jit: bool = True,
threshold: float = 1e-3,
lse_mode: bool = True,
inner_iterations: int = 10,
min_iterations: int = 0,
max_iterations: int = 2000,
device: Optional[Literal["cpu", "gpu", "tpu"]] = None,
**kwargs: Any,
) -> "TemporalProblem":
r"""Solve the temporal problem.
.. seealso:
- See :doc:`../notebooks/tutorials/200_temporal_problem` on how to
prepare and solve the :class:`~moscot.problems.time.TemporalProblem`.
Parameters
----------
epsilon
:term:`Entropic regularization`.
tau_a
Parameter in :math:`(0, 1]` that defines how much :term:`unbalanced <unbalanced OT problem>` is the problem
on the source :term:`marginals`. If :math:`1`, the problem is :term:`balanced <balanced OT problem>`.
tau_b
Parameter in :math:`(0, 1]` that defines how much :term:`unbalanced <unbalanced OT problem>` is the problem
on the target :term:`marginals`. If :math:`1`, the problem is :term:`balanced <balanced OT problem>`.
rank
Rank of the :term:`low-rank OT` solver :cite:`scetbon:21a`.
If :math:`-1`, full-rank solver :cite:`cuturi:2013` is used.
scale_cost
How to re-scale the cost matrix. If a :class:`float`, the cost matrix
will be re-scaled as :math:`\frac{\text{cost}}{\text{scale_cost}}`.
batch_size
Number of rows/columns of the cost matrix to materialize during the :term:`Sinkhorn` iterations.
Larger value will require more memory.
stage
Stage by which to filter the :attr:`problems` to be solved.
initializer
How to initialize the solution. If :obj:`None`, ``'default'`` will be used for a full-rank solver and
``'rank2'`` for a low-rank solver.
initializer_kwargs
Keyword arguments for the ``initializer``.
jit
Whether to :func:`~jax.jit` the underlying :mod:`ott` solver.
threshold
Convergence threshold of the :term:`Sinkhorn` algorithm. In the :term:`balanced <balanced OT problem>` case,
this is typically the deviation between the target :term:`marginals` and the marginals of the current
:term:`transport matrix`. In the :term:`unbalanced <unbalanced OT problem>` case, the relative change
between the successive solutions is checked.
lse_mode
Whether to use `log-sum-exp (LSE)
<https://en.wikipedia.org/wiki/LogSumExp#log-sum-exp_trick_for_log-domain_calculations>`_
computations for numerical stability.
inner_iterations
Compute the convergence criterion every ``inner_iterations``.
min_iterations
Minimum number of :term:`Sinkhorn` iterations.
max_iterations
Maximum number of :term:`Sinkhorn` iterations.
device
Transfer the solution to a different device, see :meth:`~moscot.base.output.BaseSolverOutput.to`.
If :obj:`None`, keep the output on the original device.
kwargs
Keyword arguments for :meth:`~moscot.base.problems.CompoundProblem.solve`.
Returns
-------
Returns self and updates the following fields:
- :attr:`solutions` - the :term:`OT` solutions for each subproblem.
- :attr:`stage` - set to ``'solved'``.
"""
return super().solve( # type:ignore[return-value]
epsilon=epsilon,
tau_a=tau_a,
tau_b=tau_b,
rank=rank,
scale_cost=scale_cost,
batch_size=batch_size,
stage=stage,
initializer=initializer,
initializer_kwargs=initializer_kwargs,
jit=jit,
threshold=threshold,
lse_mode=lse_mode,
inner_iterations=inner_iterations,
min_iterations=min_iterations,
max_iterations=max_iterations,
device=device,
**kwargs,
)
@property
def _base_problem_type(self) -> Type[B]:
return BirthDeathProblem # type: ignore[return-value]
@property
def _valid_policies(self) -> Tuple[Policy_t, ...]:
return _constants.SEQUENTIAL, _constants.TRIL, _constants.TRIU, _constants.EXPLICIT # type: ignore[return-value] # noqa: E501
[docs]
class LineageProblem(TemporalProblem):
"""Estimator for modelling time series single cell data based on :cite:`lange-moslin:23`.
Parameters
----------
adata
Annotated data object.
kwargs
Keyword arguments for :class:`~moscot.problems.time.TemporalProblem`.
"""
[docs]
def prepare(
self,
time_key: str,
lineage_attr: Mapping[str, Any] = types.MappingProxyType({}),
joint_attr: Optional[Union[str, Mapping[str, Any]]] = None,
policy: Literal["sequential", "triu", "tril", "explicit"] = "sequential",
cost: CostFnMap_t = "sq_euclidean",
cost_kwargs: CostKwargs_t = types.MappingProxyType({}),
a: Optional[str] = None,
b: Optional[str] = None,
marginal_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
**kwargs: Any,
) -> "LineageProblem":
"""Prepare the lineage problem problem.
.. seealso::
- See :doc:`../notebooks/tutorials/100_lineage` on how to
prepare and solve the :class:`~moscot.problems.time.LineageProblem`.
Parameters
----------
time_key
Key in :attr:`~anndata.AnnData.obs` where the time points are stored.
lineage_attr
How to get the lineage information, such as barcodes or lineage trees, for the :term:`quadratic term`:
- :class:`dict` - it should contain ``'attr'`` and ``'key'``, the attribute and key in
:class:`~anndata.AnnData`, and optionally ``'tag'`` from the
:class:`tags <moscot.utils.tagged_array.Tag>`.
If an empty :class:`dict` is passed, use pre-computed cost matrices stored in
:attr:`obsp['cost_matrices'] <anndata.AnnData.obsp>`.
joint_attr
How to get the data for the :term:`linear term` in the :term:`fused <fused Gromov-Wasserstein>` case:
- :obj:`None` - `PCA <https://en.wikipedia.org/wiki/Principal_component_analysis>`_
on :attr:`~anndata.AnnData.X` is computed.
- :class:`str` - key in :attr:`~anndata.AnnData.obsm` where the data is stored.
- :class:`dict` - it should contain ``'attr'`` and ``'key'``, the attribute and key in
:class:`~anndata.AnnData`, and optionally ``'tag'`` from the
:class:`tags <moscot.utils.tagged_array.Tag>`.
By default, :attr:`tag = 'point_cloud' <moscot.utils.tagged_array.Tag.POINT_CLOUD>` is used.
policy
Rule which defines how to construct the subproblems using :attr:`obs['{time_key}'] <anndata.AnnData.obs>`.
Valid options are:
- ``'sequential'`` - align subsequent time points ``[(t0, t1), (t1, t2), ...]``.
- ``'triu'`` - upper triangular matrix ``[(t0, t1), (t0, t2), ..., (t1, t2), ...]``.
- ``'tril'`` - lower triangular matrix ``[(t_n, t_n-1), (t_n, t0), ..., (t_n-1, t_n-2), ...]``.
- ``'explicit'`` - explicit sequence of subsets passed via ``subset = [(b3, b0), ...]``.
cost
Cost function to use. Valid options are:
- :class:`str` - name of the cost function for all terms, see :func:`~moscot.costs.get_available_costs`.
- :class:`dict` - a dictionary with the following keys and values:
- ``'xy'`` - cost function for the :term:`linear term`.
- ``'x'`` - cost function for the source :term:`quadratic term`, e.g., :func:`~moscot.costs.LeafDistance`
or :func:`~moscot.costs.BarcodeDistance`.
- ``'y'`` - cost function for the target :term:`quadratic term`, e.g., :func:`~moscot.costs.LeafDistance`
or :func:`~moscot.costs.BarcodeDistance`.
cost_kwargs
Keyword arguments for the :class:`~moscot.base.cost.BaseCost` or any backend-specific cost.
a
Source :term:`marginals`. Valid options are:
- :class:`str` - key in :attr:`~anndata.AnnData.obs` where the source marginals are stored.
- :class:`bool` - if :obj:`True`,
:meth:`estimate the marginals <moscot.base.problems.BirthDeathProblem.estimate_marginals>`,
otherwise use uniform marginals.
- :obj:`None` - set to :obj:`True` if :attr:`proliferation_key` or :attr:`apoptosis_key` is not :obj:`None`.
b
Target :term:`marginals`. Valid options are:
- :class:`str` - key in :attr:`~anndata.AnnData.obs` where the target marginals are stored.
- :class:`bool` - if :obj:`True`,
:meth:`estimate the marginals <moscot.base.problems.BirthDeathProblem.estimate_marginals>`,
otherwise use uniform marginals.
- :obj:`None` - set to :obj:`True` if :attr:`proliferation_key` or :attr:`apoptosis_key` is not :obj:`None`.
marginal_kwargs
Keyword arguments for :meth:`~moscot.base.problems.BirthDeathProblem.estimate_marginals`.
It always contains :attr:`proliferation_key` and :attr:`apoptosis_key`,
see :meth:`score_genes_for_marginals` for more information.
kwargs
Keyword arguments for :meth:`~moscot.base.problems.CompoundProblem.prepare`.
Returns
-------
Returns self and updates the following fields:
- :attr:`problems` - the prepared subproblems.
- :attr:`solutions` - set to an empty :class:`dict`.
- :attr:`temporal_key` - key in :attr:`~anndata.AnnData.obs` where time points are stored.
- :attr:`stage` - set to ``'prepared'``.
- :attr:`problem_kind` - set to ``'quadratic'``.
"""
if not len(lineage_attr) and ("cost_matrices" not in self.adata.obsp):
raise KeyError("Unable to find cost matrices in `adata.obsp['cost_matrices']`.")
x = y = lineage_attr
xy, kwargs = handle_joint_attr(joint_attr, kwargs)
xy, x, y = handle_cost(xy=xy, x=x, y=y, cost=cost, cost_kwargs=cost_kwargs, **kwargs)
x.setdefault("attr", "obsp")
x.setdefault("key", "cost_matrices")
x.setdefault("cost", "custom")
x.setdefault("tag", "cost_matrix")
y.setdefault("attr", "obsp")
y.setdefault("key", "cost_matrices")
y.setdefault("cost", "custom")
y.setdefault("tag", "cost_matrix")
return super().prepare( # type: ignore[return-value]
time_key,
joint_attr=xy,
x=x,
y=y,
policy=policy,
cost=cost, # type: ignore[arg-type]
a=a,
b=b,
marginal_kwargs=marginal_kwargs,
**kwargs,
)
[docs]
def solve(
self,
alpha: float = 0.5,
epsilon: float = 1e-3,
tau_a: float = 1.0,
tau_b: float = 1.0,
rank: int = -1,
scale_cost: ScaleCost_t = "mean",
batch_size: Optional[int] = None,
stage: Union[ProblemStage_t, Tuple[ProblemStage_t, ...]] = ("prepared", "solved"),
initializer: QuadInitializer_t = None,
initializer_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
jit: bool = True,
min_iterations: int = 5,
max_iterations: int = 50,
threshold: float = 1e-3,
linear_solver_kwargs: Mapping[str, Any] = types.MappingProxyType({}),
device: Optional[Literal["cpu", "gpu", "tpu"]] = None,
**kwargs: Any,
) -> "LineageProblem":
r"""Solve the lineage problem.
.. seealso::
- See :doc:`../notebooks/tutorials/100_lineage` on how to
prepare and solve the :class:`~moscot.problems.time.LineageProblem`.
Parameters
----------
alpha
Parameter in :math:`(0, 1]` that interpolates between the :term:`quadratic term` and
the :term:`linear term`. :math:`\alpha = 1` corresponds to the pure :term:`Gromov-Wasserstein` problem while
:math:`\alpha \to 0` corresponds to the pure :term:`linear problem`.
epsilon
:term:`Entropic regularization`.
tau_a
Parameter in :math:`(0, 1]` that defines how much :term:`unbalanced <unbalanced OT problem>` is the problem
on the source :term:`marginals`. If :math:`1`, the problem is :term:`balanced <balanced OT problem>`.
tau_b
Parameter in :math:`(0, 1]` that defines how much :term:`unbalanced <unbalanced OT problem>` is the problem
on the target :term:`marginals`. If :math:`1`, the problem is :term:`balanced <balanced OT problem>`.
rank
Rank of the :term:`low-rank OT` solver :cite:`scetbon:21b`.
If :math:`-1`, full-rank solver :cite:`peyre:2016` is used.
scale_cost
How to re-scale the cost matrices. If a :class:`float`, the cost matrices
will be re-scaled as :math:`\frac{\text{cost}}{\text{scale_cost}}`.
batch_size
Number of rows/columns of the cost matrix to materialize during the solver iterations.
Larger value will require more memory.
stage
Stage by which to filter the :attr:`problems` to be solved.
initializer
How to initialize the solution. If :obj:`None`, ``'default'`` will be used for a full-rank solver and
``'rank2'`` for a low-rank solver.
initializer_kwargs
Keyword arguments for the ``initializer``.
jit
Whether to :func:`~jax.jit` the underlying :mod:`ott` solver.
min_iterations
Minimum number of :term:`(fused) GW <Gromov-Wasserstein>` iterations.
max_iterations
Maximum number of :term:`(fused) GW <Gromov-Wasserstein>` iterations.
threshold
Convergence threshold of the :term:`GW <Gromov-Wasserstein>` solver.
linear_solver_kwargs
Keyword arguments for the inner :term:`linear problem` solver.
device
Transfer the solution to a different device, see :meth:`~moscot.base.output.BaseSolverOutput.to`.
If :obj:`None`, keep the output on the original device.
kwargs
Keyword arguments for :meth:`~moscot.problems.time.TemporalProblem.solve`.
Returns
-------
Returns self and updates the following fields:
- :attr:`solutions` - the :term:`OT` solutions for each subproblem.
- :attr:`stage` - set to ``'solved'``.
"""
return super().solve( # type: ignore[return-value]
alpha=alpha,
epsilon=epsilon,
tau_a=tau_a,
tau_b=tau_b,
rank=rank,
scale_cost=scale_cost,
batch_size=batch_size,
stage=stage,
initializer=initializer,
initializer_kwargs=initializer_kwargs,
jit=jit,
min_iterations=min_iterations,
max_iterations=max_iterations,
threshold=threshold,
linear_solver_kwargs=linear_solver_kwargs,
device=device,
**kwargs,
)