spateo.alignment.transform¶
Functions¶
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Align the space coordinates of the new model with the transformation matrix obtained from PASTE. |
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Apply non-rigid transform to the quary points |
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Calculating the generating probability matrix P. |
Module Contents¶
- spateo.alignment.transform.paste_transform(adata: anndata.AnnData, adata_ref: anndata.AnnData, spatial_key: str = 'spatial', key_added: str = 'align_spatial', mapping_key: str = 'models_align') anndata.AnnData [source]¶
Align the space coordinates of the new model with the transformation matrix obtained from PASTE.
- Parameters:
- adata
The anndata object that need to be aligned.
- adata_ref
The anndata object that have been aligned by PASTE.
- spatial_key
The key in .obsm that corresponds to the raw spatial coordinates.
- key_added
.obsm
key under which to add the aligned spatial coordinates.- mapping_key
The key in .uns that corresponds to the alignment info from PASTE.
- Returns:
The anndata object that have been to be aligned.
- Return type:
adata
- spateo.alignment.transform.BA_transform(vecfld, quary_points, deformation_scale: int = 1, dtype: str = 'float64', device: str = 'cpu')[source]¶
Apply non-rigid transform to the quary points
- Parameters:
- vecfld
A dictionary containing information about vector fields
- quary_points
- deformation_scale
If deformation_scale is greater than 1, increase the degree of deformation.
- dtype
The floating-point number type. Only
float32
andfloat64
.- device
Equipment used to run the program. You can also set the specified GPU for running.
E.g.: '0'
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- spateo.alignment.transform.BA_transform_and_assignment(samples, vecfld, layer: str = 'X', genes: List | torch.Tensor | None = None, spatial_key: str = 'spatial', small_variance: bool = False, dtype: str = 'float64', device: str = 'cpu', verbose: bool = False)[source]¶
- spateo.alignment.transform.get_P_chunk(XnAHat: numpy.ndarray | torch.Tensor, XnB: numpy.ndarray | torch.Tensor, X_A: numpy.ndarray | torch.Tensor, X_B: numpy.ndarray | torch.Tensor, sigma2: int | float | numpy.ndarray | torch.Tensor, beta2: int | float | numpy.ndarray | torch.Tensor, alpha: numpy.ndarray | torch.Tensor, gamma: float | numpy.ndarray | torch.Tensor, Sigma: numpy.ndarray | torch.Tensor, samples_s: List[float] | None = None, outlier_variance: float = None, chunk_size: int = 1000, dissimilarity: str = 'kl') numpy.ndarray | torch.Tensor [source]¶
Calculating the generating probability matrix P.
- Parameters:
- XAHat
Current spatial coordinate of sample A. Shape