moscot.problems.space.AlignmentProblem.prepare

AlignmentProblem.prepare(batch_key, spatial_key='spatial', joint_attr=None, policy='sequential', reference=None, normalize_spatial=True, cost='sq_euclidean', cost_kwargs=mappingproxy({}), a=None, b=None, xy_callback=None, x_callback=None, y_callback=None, xy_callback_kwargs=mappingproxy({}), x_callback_kwargs=mappingproxy({}), y_callback_kwargs=mappingproxy({}), subset=None, marginal_kwargs=mappingproxy({}))

Prepare the alignment problem problem.

See also

• See Alignment of spatial transcriptomics data on how to prepare and solve the AlignmentProblem.

Parameters:

• batch_key (str) – Key in obs where the slices are stored.

• spatial_key (str) – Key in obsm where the spatial coordinates are stored.

• joint_attr (Union[str, Mapping[str, Any], None]) –

  How to get the data for the linear term in the fused case:

  • None - PCA on X is computed.

  • str - key in obsm where the data is stored.

  • dict - it should contain 'attr' and 'key', the attribute and key in AnnData, and optionally 'tag' from the tags.

  By default, tag = 'point_cloud' is used.

• policy (Literal['sequential', 'star']) –

  Rule which defines how to construct the subproblems using obs['{batch_key}']. Valid options are:

  • 'sequential' - align subsequent slices.

  • 'star' - align all slices to the reference.

• reference (Optional[str]) – Spatial reference when policy = 'star'.

• normalize_spatial (bool) – Whether to normalize the spatial coordinates. If True, the coordinates are normalized by standardizing them.

• cost (Union[Literal['euclidean', 'sq_euclidean', 'cosine', 'pnorm_p', 'sq_pnorm', 'geodesic'], Mapping[Literal['xy', 'x', 'y'], Literal['euclidean', 'sq_euclidean', 'cosine', 'pnorm_p', 'sq_pnorm', 'geodesic']]]) –

  Cost function to use. Valid options are:

  • str - name of the cost function for all terms, see get_available_costs().

  • dict - a dictionary with the following keys and values:

    • 'xy' - cost function for the linear term.

    • 'x' - cost function for the source quadratic term.

    • 'y' - cost function for the target quadratic term.

• cost_kwargs (Union[Mapping[str, Any], Mapping[Literal['x', 'y', 'xy'], Mapping[str, Any]]]) – Keyword arguments for the BaseCost or any backend-specific cost.

• a (Union[bool, str, None]) –

  Source marginals. Valid options are:

  • str - key in obs where the source marginals are stored.

  • bool - if True, estimate the marginals, otherwise use uniform marginals.

  • None - uniform marginals.

• b (Union[bool, str, None]) –

  Target marginals. Valid options are:

  • str - key in obs where the target marginals are stored.

  • bool - if True, estimate the marginals, otherwise use uniform marginals.

  • None - uniform marginals.

• xy_callback (Literal['local-pca'] | ~typing.Callable[[~typing.Literal['xy', 'x', 'y'], ~anndata._core.anndata.AnnData, ~anndata._core.anndata.AnnData | None], ~moscot.utils.tagged_array.TaggedArray | None] | None)

• x_callback (Literal['local-pca'] | ~typing.Callable[[~typing.Literal['xy', 'x', 'y'], ~anndata._core.anndata.AnnData, ~anndata._core.anndata.AnnData | None], ~moscot.utils.tagged_array.TaggedArray | None] | None)

• y_callback (Literal['local-pca'] | ~typing.Callable[[~typing.Literal['xy', 'x', 'y'], ~anndata._core.anndata.AnnData, ~anndata._core.anndata.AnnData | None], ~moscot.utils.tagged_array.TaggedArray | None] | None)

• xy_callback_kwargs (Mapping[str, Any])

• x_callback_kwargs (Mapping[str, Any])

• y_callback_kwargs (Mapping[str, Any])

• subset (Sequence[Tuple[K, K]] | None)

• marginal_kwargs (Mapping[str, Any])

Return type:

AlignmentProblem[TypeVar(K, bound= Hashable), TypeVar(B, bound= OTProblem)]

Returns:

: Returns self and updates the following fields:

• problems - the prepared subproblems.

• solutions - set to an empty dict.

• spatial_key - key in obsm where the spatial coordinates are stored.

• batch_key - key in obs where batches are stored.

• stage - set to 'prepared'.

• problem_kind - set to 'quadratic'.