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