`spateo.alignment.utils`#

Module Contents#

`_iteration`(n, progress_name[, verbose])
`downsampling`(→ Union[List[anndata.AnnData], ...)
`get_optimal_mapping_relationship`(X, Y, pi[, keep_all])
`mapping_aligned_coords`(→ Tuple[dict, dict])	Optimal mapping coordinates between X and Y.
`mapping_center_coords`(→ dict)	Optimal mapping coordinates between X and Y based on intermediate coordinates.
`get_labels_based_on_coords`(→ pandas.DataFrame)	Obtain the label information in anndata.obs[key] corresponding to the coords.

spateo.alignment.utils._iteration(n: int, progress_name: str, verbose: bool = True)[source]#

spateo.alignment.utils.downsampling(models: List[anndata.AnnData] | anndata.AnnData, n_sampling: int | None = 2000, sampling_method: str = 'trn', spatial_key: str = 'spatial') → List[anndata.AnnData] | anndata.AnnData[source]#

spateo.alignment.utils.get_optimal_mapping_relationship(X: numpy.ndarray, Y: numpy.ndarray, pi: numpy.ndarray, keep_all: bool = False)[source]#

spateo.alignment.utils.mapping_aligned_coords(X: numpy.ndarray, Y: numpy.ndarray, pi: numpy.ndarray, keep_all: bool = False) → Tuple[dict, dict][source]#

Optimal mapping coordinates between X and Y.

Parameters:

X: Aligned spatial coordinates.
Y: Aligned spatial coordinates.
pi: Mapping between the two layers output by PASTE.
keep_all: Whether to retain all the optimal relationships obtained only based on the pi matrix, If keep_all is False, the optimal relationships obtained based on the pi matrix and the nearest coordinates.

Returns:

Two dicts of mapping_X, mapping_Y, pi_index, pi_value.: mapping_X is X coordinates aligned with Y coordinates. mapping_Y is the Y coordinate aligned with X coordinates. pi_index is index between optimal mapping points in the pi matrix. pi_value is the value of optimal mapping points.

spateo.alignment.utils.mapping_center_coords(modelA: anndata.AnnData, modelB: anndata.AnnData, center_key: str) → dict[source]#

Optimal mapping coordinates between X and Y based on intermediate coordinates.

Parameters:

modelA: modelA aligned with center model.
modelB: modelB aligned with center model.
center_key: The key in .uns that corresponds to the alignment info between modelA/modelB and center model.

Returns:

A dict of raw_X, raw_Y, mapping_X, mapping_Y, pi_value.: raw_X is the raw X coordinates. raw_Y is the raw Y coordinates. mapping_X is the Y coordinates aligned with X coordinates. mapping_Y is the X coordinates aligned with Y coordinates. pi_value is the value of optimal mapping points.

spateo.alignment.utils.get_labels_based_on_coords(model: anndata.AnnData, coords: numpy.ndarray, labels_key: str | List[str], spatial_key: str = 'align_spatial') → pandas.DataFrame[source]#: Obtain the label information in anndata.obs[key] corresponding to the coords.