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Code modifications to work with ot.TorchBackend (#77)
* Code modifications to work with ot.TorchBackend; changed backend for align submodule
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,145 @@ | ||
| from typing import Optional, List, Tuple | ||
| from pathlib import Path | ||
| import scanpy as sc | ||
| import numpy as np | ||
| from anndata import AnnData | ||
| import logging | ||
| from paste3.helper import match_spots_using_spatial_heuristic | ||
| from paste3.visualization import stack_slices_pairwise, stack_slices_center | ||
| from paste3.paste import pairwise_align, center_align | ||
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| logger = logging.getLogger(__name__) | ||
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| class Slice: | ||
| def __init__( | ||
| self, filepath: Optional[Path] = None, adata: Optional[AnnData] = None | ||
| ): | ||
| if adata is None: | ||
| self.adata = sc.read_h5ad(filepath) | ||
| else: | ||
| self.adata = adata | ||
|
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| def __str__(self): | ||
| return f"Slice {self.adata}" | ||
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| class AlignmentDataset: | ||
| @staticmethod | ||
| def from_csvs(gene_expression_csvs: List[Path], coordinate_csvs: List[Path]): | ||
| pass | ||
|
|
||
| def __init__( | ||
| self, | ||
| data_dir: Optional[Path] = None, | ||
| slices: Optional[List[Slice]] = None, | ||
| max_slices: Optional[int] = None, | ||
| ): | ||
| if slices is not None: | ||
| self.slices = slices[:max_slices] | ||
| else: | ||
| self.slices = [ | ||
| Slice(filepath) | ||
| for filepath in sorted(Path(data_dir).glob("*.h5ad"))[:max_slices] | ||
| ] | ||
|
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||
| def __str__(self): | ||
| return f"Data with {len(self.slices)} slices" | ||
|
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| def __iter__(self): | ||
| return iter(self.slices) | ||
|
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| def __len__(self): | ||
| return len(self.slices) | ||
|
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| @property | ||
| def slices_adata(self) -> List[AnnData]: | ||
| return [slice_.adata for slice_ in self.slices] | ||
|
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| def align( | ||
| self, | ||
| center_align: bool = False, | ||
| center_slice: Optional[Slice] = None, | ||
| pis: Optional[np.ndarray] = None, | ||
| overlap_fraction: Optional[float] = None, | ||
| max_iters: int = 1000, | ||
| ): | ||
| if center_align: | ||
| if overlap_fraction is not None: | ||
| logger.warning( | ||
| "Ignoring overlap_fraction argument (unsupported in center_align mode)" | ||
| ) | ||
| return self.center_align(center_slice, pis) | ||
| else: | ||
| assert overlap_fraction is not None, "overlap_fraction must be specified" | ||
| return self.pairwise_align( | ||
| overlap_fraction=overlap_fraction, pis=pis, max_iters=max_iters | ||
| ) | ||
|
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||
| def find_pis(self, overlap_fraction: float, max_iters: int = 1000): | ||
| pis = [] | ||
| for i in range(len(self) - 1): | ||
| logger.info(f"Finding Pi for slices {i} and {i+1}") | ||
| pis.append( | ||
| pairwise_align( | ||
| self.slices[i].adata, | ||
| self.slices[i + 1].adata, | ||
| s=overlap_fraction, | ||
| numItermax=max_iters, | ||
| maxIter=max_iters, | ||
| verbose=True, | ||
| ) | ||
| ) | ||
| return pis | ||
|
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||
| def pairwise_align( | ||
| self, | ||
| overlap_fraction: float, | ||
| pis: Optional[List[np.ndarray]] = None, | ||
| max_iters: int = 1000, | ||
| ): | ||
| if pis is None: | ||
| pis = self.find_pis(overlap_fraction=overlap_fraction, max_iters=max_iters) | ||
| new_slices = stack_slices_pairwise(self.slices_adata, pis) | ||
| return AlignmentDataset(slices=[Slice(adata=s) for s in new_slices]) | ||
|
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||
| def find_center_slice( | ||
| self, reference_slice: Optional[Slice] = None, pis: Optional[np.ndarray] = None | ||
| ) -> Tuple[Slice, List[np.ndarray]]: | ||
| if reference_slice is None: | ||
| reference_slice = self.slices[0] | ||
| center_slice, pis = center_align( | ||
| reference_slice.adata, self.slices_adata, pis_init=pis | ||
| ) | ||
| return Slice(adata=center_slice), pis | ||
|
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| def find_pis_init(self) -> List[np.ndarray]: | ||
| reference_slice = self.slices[0] | ||
| return [ | ||
| match_spots_using_spatial_heuristic(reference_slice.adata.X, slice_.adata.X) | ||
| for slice_ in self.slices | ||
| ] | ||
|
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||
| def center_align( | ||
| self, | ||
| reference_slice: Optional[Slice] = None, | ||
| pis: Optional[List[np.ndarray]] = None, | ||
| ): | ||
| if reference_slice is None: | ||
| reference_slice, pis = self.find_center_slice(pis=pis) | ||
| else: | ||
| pis = self.find_pis_init() | ||
|
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||
| _, new_slices = stack_slices_center( | ||
| center_slice=reference_slice.adata, slices=self.slices_adata, pis=pis | ||
| ) | ||
| return AlignmentDataset(slices=[Slice(adata=s) for s in new_slices]) | ||
|
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| if __name__ == "__main__": | ||
| dataset = AlignmentDataset("data/", max_slices=3) | ||
| aligned_dataset = dataset.align( | ||
| center_align=False, overlap_fraction=0.7, max_iters=2 | ||
| ) | ||
| print(aligned_dataset) |
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