Fix the bugs for paste align: add paste_align_preprocess back

spateo/alignment/methods/__init__.py

@@ -2,6 +2,7 @@
 # from .morpho_sparse import BA_align_sparse
 from .backend import NumpyBackend, TorchBackend
 from .deprecated_utils import (
+    paste_align_preprocess,
     align_preprocess,
     cal_dist,
     cal_dot,

spateo/alignment/methods/backend.py

@@ -1067,7 +1067,7 @@ def data(self, a, type_as=None):
         else:
             return np.asarray(a, dtype=type_as.dtype)
 
-    def unique(self, a, return_index, return_inverse=False, axis=None):
+    def unique(self, a, return_index=False, return_inverse=False, axis=None):
         return np.unique(a, return_index=return_index, return_inverse=return_inverse, axis=axis)
 
     def unsqueeze(self, a, axis=-1):

spateo/alignment/methods/deprecated_utils.py

@@ -728,6 +728,96 @@ def align_preprocess(
     )
 
 
+def paste_align_preprocess(
+    samples: List[AnnData],
+    genes: Optional[Union[list, np.ndarray]] = None,
+    spatial_key: str = "spatial",
+    layer: str = "X",
+    use_rep: Optional[str] = None,
+    normalize_c: bool = False,
+    normalize_g: bool = False,
+    select_high_exp_genes: Union[bool, float, int] = False,
+    dtype: str = "float64",
+    device: str = "cpu",
+    verbose: bool = True,
+    **kwargs,
+) -> Tuple[
+    TorchBackend or NumpyBackend,
+    torch.Tensor or np.ndarray,
+    list,
+    list,
+    list,
+    Optional[float],
+    Optional[list],
+]:
+    """
+    Data preprocessing before alignment.
+
+    Args:
+        samples: A list of anndata object.
+        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.
+        layer: If `'X'`, uses ``sample.X`` to calculate dissimilarity between spots, otherwise uses the representation given by ``sample.layers[layer]``.
+        normalize_c: Whether to normalize spatial coordinates.
+        normalize_g: Whether to normalize gene expression.
+        select_high_exp_genes: Whether to select genes with high differences in gene expression.
+        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.
+    """
+
+    # Determine if gpu or cpu is being used
+    nx, type_as = check_backend(device=device, dtype=dtype)
+    # Subset for common genes
+    new_samples = [s.copy() for s in samples]
+    all_samples_genes = [s[0].var.index for s in new_samples]
+    common_genes = filter_common_genes(*all_samples_genes, verbose=verbose)
+    common_genes = common_genes if genes is None else intersect_lsts(common_genes, genes)
+    new_samples = [s[:, common_genes] for s in new_samples]
+
+    # Gene expression matrix of all samples
+    if (use_rep is None) or (not isinstance(use_rep, str)) or (use_rep not in samples[0].obsm.keys()) or (use_rep not in samples[1].obsm.keys()):
+        exp_matrices = [nx.from_numpy(check_exp(sample=s, layer=layer), type_as=type_as) for s in new_samples]
+    else:
+        exp_matrices = [nx.from_numpy(s.obsm[use_rep], type_as=type_as) for s in new_samples] + [nx.from_numpy(check_exp(sample=s, layer=layer), type_as=type_as) for s in new_samples]
+    if not (select_high_exp_genes is False):
+        # Select significance genes if select_high_exp_genes is True
+        ExpressionData = _cat(nx=nx, x=exp_matrices, dim=0)
+
+        ExpressionVar = _var(nx, ExpressionData, 0)
+        exp_threshold = 10 if isinstance(select_high_exp_genes, bool) else select_high_exp_genes
+        EvidenceExpression = nx.where(ExpressionVar > exp_threshold)[0]
+        exp_matrices = [exp_matrix[:, EvidenceExpression] for exp_matrix in exp_matrices]
+        if verbose:
+            lm.main_info(message=f"Evidence expression number: {len(EvidenceExpression)}.")
+
+    # Spatial coordinates of all samples
+    spatial_coords = [
+        nx.from_numpy(check_spatial_coords(sample=s, spatial_key=spatial_key), type_as=type_as) for s in new_samples
+    ]
+    coords_dims = nx.unique(_data(nx, [c.shape[1] for c in spatial_coords], type_as))
+    # coords_dims = np.unique(np.asarray([c.shape[1] for c in spatial_coords]))
+    assert len(coords_dims) == 1, "Spatial coordinate dimensions are different, please check again."
+
+    normalize_scale_list, normalize_mean_list = None, None
+    if normalize_c:
+        spatial_coords, normalize_scale_list, normalize_mean_list = normalize_coords(
+            coords=spatial_coords, nx=nx, verbose=verbose
+        )
+    if normalize_g and ((use_rep is None) or (not isinstance(use_rep, str)) or (use_rep not in samples[0].obsm.keys()) or (use_rep not in samples[1].obsm.keys())):
+        exp_matrices = normalize_exps(matrices=exp_matrices, nx=nx, verbose=verbose)
+
+    return (
+        nx,
+        type_as,
+        new_samples,
+        exp_matrices,
+        spatial_coords,
+        normalize_scale_list,
+        normalize_mean_list,
+    )
+
+
 # Finished
 def guidance_pair_preprocess(
     nx: Union[TorchBackend, NumpyBackend],

spateo/alignment/methods/paste.py

@@ -9,6 +9,7 @@
 from spateo.logging import logger_manager as lm
 
 from .deprecated_utils import (
+    paste_align_preprocess,
     align_preprocess,
     calc_exp_dissimilarity,
     check_exp,
@@ -70,7 +71,7 @@ def paste_pairwise_align(
     """
 
     # Preprocessing
-    (nx, type_as, new_samples, exp_matrices, spatial_coords, normalize_scale, normalize_mean_list,) = align_preprocess(
+    (nx, type_as, new_samples, exp_matrices, spatial_coords, normalize_scale, normalize_mean_list,) = paste_align_preprocess(
         samples=[sampleA, sampleB],
         genes=genes,
         spatial_key=spatial_key,
@@ -116,7 +117,7 @@ def paste_pairwise_align(
     except ImportError:
         from ot.gromov import cg
 
-    pi, log = ot.gromov.cg(
+    pi, log = cg(
         a,
         b,
         (1 - alpha) * M,