spateo.alignment.methods.paste#

Module Contents#

Functions#

paste_pairwise_align(→ Tuple[numpy.ndarray, Optional[int]])

Calculates and returns optimal alignment of two slices.

center_NMF(n_components, random_seed[, dissimilarity])

paste_center_align(→ Tuple[anndata.AnnData, ...)

Computes center alignment of slices.

generalized_procrustes_analysis(X, Y, pi)

Finds and applies optimal rotation between spatial coordinates of two layers (may also do a reflection).
spateo.alignment.methods.paste.paste_pairwise_align(sampleA: anndata.AnnData, sampleB: anndata.AnnData, layer: str = 'X', genes: list | numpy.ndarray | None = None, spatial_key: str = 'spatial', alpha: float = 0.1, dissimilarity: str = 'kl', G_init=None, a_distribution=None, b_distribution=None, norm: bool = False, numItermax: int = 200, numItermaxEmd: int = 100000, dtype: str = 'float32', device: str = 'cpu', verbose: bool = True) → Tuple[numpy.ndarray, int | None][source]#

Calculates and returns optimal alignment of two slices.

Parameters:
sampleA

Sample A to align.

sampleB

Sample B to align.

layer

If ‘X’, uses sample.X to calculate dissimilarity between spots, otherwise uses the representation given by sample.layers[layer].

genes

Genes used for calculation. If None, use all common genes for calculation.

spatial_key

The key in .obsm that corresponds to the raw spatial coordinates.

alpha

Alignment tuning parameter. Note: 0 <= alpha <= 1. When α = 0 only the gene expression data is taken into account, while when α =1 only the spatial coordinates are taken into account.

dissimilarity

Expression dissimilarity measure: 'kl' or 'euclidean'.

G_init

Initial mapping to be used in FGW-OT, otherwise default is uniform mapping.

a_distribution

Distribution of sampleA spots, otherwise default is uniform.

b_distribution

Distribution of sampleB spots, otherwise default is uniform.

norm

If True, scales spatial distances such that neighboring spots are at distance 1. Otherwise, spatial distances remain unchanged.

numItermax

Max number of iterations for cg during FGW-OT.

numItermaxEmd

Max number of iterations for emd during FGW-OT.

dtype

The floating-point number type. Only float32 and float64.

device

Equipment used to run the program. You can also set the specified GPU for running. E.g.: ‘0’.

verbose

If True, print progress updates.

Returns:

Alignment of spots. obj: Objective function output of FGW-OT.

Return type:

pi

spateo.alignment.methods.paste.center_NMF(n_components, random_seed, dissimilarity='kl')[source]#
spateo.alignment.methods.paste.paste_center_align(init_center_sample: anndata.AnnData, samples: List[anndata.AnnData], layer: str = 'X', genes: list | numpy.ndarray | None = None, spatial_key: str = 'spatial', lmbda: numpy.ndarray | None = None, alpha: float = 0.1, n_components: int = 15, threshold: float = 0.001, max_iter: int = 10, numItermax: int = 200, numItermaxEmd: int = 100000, dissimilarity: str = 'kl', norm: bool = False, random_seed: int | None = None, pis_init: List[numpy.ndarray] | None = None, distributions: List[numpy.ndarray] | None = None, dtype: str = 'float32', device: str = 'cpu', verbose: bool = True) → Tuple[anndata.AnnData, List[numpy.ndarray]][source]#

Computes center alignment of slices.

Parameters:
init_center_sample

Sample to use as the initialization for center alignment; Make sure to include gene expression and spatial information.

samples

List of samples to use in the center alignment.

layer

If ‘X’, uses sample.X to calculate dissimilarity between spots, otherwise uses the representation given by sample.layers[layer].

genes

Genes used for calculation. If None, use all common genes for calculation.

spatial_key

The key in .obsm that corresponds to the raw spatial coordinates.

lmbda

List of probability weights assigned to each slice; If None, use uniform weights.

alpha

Alignment tuning parameter. Note: 0 <= alpha <= 1. When α = 0 only the gene expression data is taken into account, while when α =1 only the spatial coordinates are taken into account.

n_components

Number of components in NMF decomposition.

threshold

Threshold for convergence of W and H during NMF decomposition.

max_iter

Maximum number of iterations for our center alignment algorithm.

numItermax

Max number of iterations for cg during FGW-OT.

numItermaxEmd

Max number of iterations for emd during FGW-OT.

dissimilarity

Expression dissimilarity measure: 'kl' or 'euclidean'.

norm

If True, scales spatial distances such that neighboring spots are at distance 1. Otherwise, spatial distances remain unchanged.

random_seed

Set random seed for reproducibility.

pis_init

Initial list of mappings between ‘A’ and ‘slices’ to solver. Otherwise, default will automatically calculate mappings.

distributions

Distributions of spots for each slice. Otherwise, default is uniform.

dtype

The floating-point number type. Only float32 and float64.

device

Equipment used to run the program. You can also set the specified GPU for running. E.g.: ‘0’.

verbose

If True, print progress updates.

Returns:

  • Inferred center sample with full and low dimensional representations (W, H) of the gene expression matrix.

  • List of pairwise alignment mappings of the center sample (rows) to each input sample (columns).

spateo.alignment.methods.paste.generalized_procrustes_analysis(X, Y, pi)[source]#

Finds and applies optimal rotation between spatial coordinates of two layers (may also do a reflection).

Parameters:
X

np array of spatial coordinates.

Y

np array of spatial coordinates.

pi

mapping between the two layers output by PASTE.

Returns:

Aligned spatial coordinates of X, Y and the mapping relations.