moscot.problems.space.MappingProblem.prepare

MappingProblem.prepare(sc_attr, batch_key=None, spatial_key='spatial', var_names=None, normalize_spatial=True, joint_attr=None, cost='sq_euclidean', cost_kwargs=mappingproxy({}), a=None, b=None, **kwargs)

Prepare the mapping problem problem.

See also

• See Mapping gene expression in space on how to prepare and solve the MappingProblem.

Parameters:

• sc_attr (Union[str, Mapping[str, Any]]) –

  How to get the data for the quadratic term. Usually, it’s the X attribute, which contains normalized counts, but a different modality or a pre-computed PCA can also be used. Valid options are:

  • str - a key in obsm.

  • 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.

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

• spatial_key (Union[str, Mapping[str, Any]]) – Key in obsm where the spatial coordinates are stored.

• var_names (Optional[Sequence[str]]) –

  Genes in var_names for the linear term in the fused case. Valid options are:

  • None - use all genes shared between adata_sp and adata_sc.

  • Sequence - use a subset of genes. If an empty sequence, the problem will correspond to the pure Gromov-Wasserstein case.

  See also the joint_attribute parameter.

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

• 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.

• cost (Union[Literal['euclidean', 'sq_euclidean', 'cosine', 'pnorm_p', 'sq_pnorm', 'elastic_l1', 'elastic_l2', 'elastic_stvs', 'elastic_sqk_overlap', 'geodesic'], Mapping[Literal['xy', 'x', 'y'], Literal['euclidean', 'sq_euclidean', 'cosine', 'pnorm_p', 'sq_pnorm', 'elastic_l1', 'elastic_l2', 'elastic_stvs', 'elastic_sqk_overlap', '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 (Optional[str]) –

  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 (Optional[str]) –

  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.

• kwargs (Any) – Keyword arguments for prepare().

Return type:

MappingProblem[TypeVar(K, bound= Hashable)]

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'.