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| # https://www.10xgenomics.com/datasets/fresh-frozen-mouse-brain-replicates-1-standard | ||
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| import spatialdata as sd | ||
| from spatialdata_io import xenium | ||
| from pathlib import Path | ||
| import shutil | ||
| import os | ||
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| ## VIASH START | ||
| # https://cf.10xgenomics.com/samples/xenium/1.0.2/Xenium_V1_FF_Mouse_Brain_MultiSection_1/Xenium_V1_FF_Mouse_Brain_MultiSection_1_outs.zip | ||
| # https://cf.10xgenomics.com/samples/xenium/1.0.2/Xenium_V1_FF_Mouse_Brain_MultiSection_2/Xenium_V1_FF_Mouse_Brain_MultiSection_2_outs.zip | ||
| # https://cf.10xgenomics.com/samples/xenium/1.0.2/Xenium_V1_FF_Mouse_Brain_MultiSection_3/Xenium_V1_FF_Mouse_Brain_MultiSection_3_outs.zip | ||
| par = { | ||
| "input": [ | ||
| "resources/datasets_raw/10x_fresh_frozen_mouse_brain_replicates/Xenium_V1_FF_Mouse_Brain_MultiSection_1_outs" | ||
| "resources/datasets_raw/10x_fresh_frozen_mouse_brain_replicates/Xenium_V1_FF_Mouse_Brain_MultiSection_1_outs", | ||
| "resources/datasets_raw/10x_fresh_frozen_mouse_brain_replicates/Xenium_V1_FF_Mouse_Brain_MultiSection_2_outs", | ||
| "resources/datasets_raw/10x_fresh_frozen_mouse_brain_replicates/Xenium_V1_FF_Mouse_Brain_MultiSection_3_outs", | ||
| ], | ||
| "replicate_id": [ | ||
| "rep1" | ||
| "rep1", | ||
| "rep2", | ||
| "rep3", | ||
| ], | ||
| "output": "resources/datasets/10x_xenium/10x_fresh_frozen_mouse_brain_replicates/dataset.zarr" | ||
| } | ||
| ## VIASH END | ||
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| input = Path(par["input"]).resolve() | ||
| output = Path(par["output"]).resolve() | ||
| sdatas = [] | ||
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| print("parsing the data... ", end="", flush=True) | ||
| sdata = xenium( | ||
| path=str(input), | ||
| n_jobs=8, | ||
| cell_boundaries=True, | ||
| nucleus_boundaries=True, | ||
| morphology_focus=True, | ||
| cells_as_circles=True, | ||
| ) | ||
| for i, input in enumerate(par["input"]): | ||
| print("parsing the data... ", end="", flush=True) | ||
| replicate_id = par["replicate_id"][i] | ||
| sdata = xenium( | ||
| path=input, | ||
| n_jobs=8, | ||
| cell_boundaries=True, | ||
| nucleus_boundaries=True, | ||
| morphology_focus=True, | ||
| cells_as_circles=True, | ||
| ) | ||
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| print("writing the data... ", end="", flush=True) | ||
| if output.exists(): | ||
| shutil.rmtree(output) | ||
| sdata.write(output) | ||
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| # SpatialData object | ||
| # ├── Images | ||
| # │ ├── 'morphology_focus': DataTree[cyx] (1, 33131, 48358), (1, 16565, 24179), (1, 8282, 12089), (1, 4141, 6044), (1, 2070, 3022) | ||
| # │ └── 'morphology_mip': DataTree[cyx] (1, 33131, 48358), (1, 16565, 24179), (1, 8282, 12089), (1, 4141, 6044), (1, 2070, 3022) | ||
| # ├── Labels | ||
| # │ ├── 'cell_labels': DataTree[yx] (33131, 48358), (16565, 24179), (8282, 12089), (4141, 6044), (2070, 3022) | ||
| # │ └── 'nucleus_labels': DataTree[yx] (33131, 48358), (16565, 24179), (8282, 12089), (4141, 6044), (2070, 3022) | ||
| # ├── Points | ||
| # │ └── 'transcripts': DataFrame with shape: (<Delayed>, 8) (3D points) | ||
| # ├── Shapes | ||
| # │ ├── 'cell_boundaries': GeoDataFrame shape: (162033, 1) (2D shapes) | ||
| # │ ├── 'cell_circles': GeoDataFrame shape: (162033, 2) (2D shapes) | ||
| # │ └── 'nucleus_boundaries': GeoDataFrame shape: (162033, 1) (2D shapes) | ||
| # └── Tables | ||
| # └── 'table': AnnData (162033, 248) | ||
| # with coordinate systems: | ||
| # ▸ 'global', with elements: | ||
| # morphology_focus (Images), morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) | ||
| # SpatialData object | ||
| # ├── Images | ||
| # │ ├── 'morphology_focus': DataTree[cyx] (1, 33131, 48358), (1, 16565, 24179), (1, 8282, 12089), (1, 4141, 6044), (1, 2070, 3022) | ||
| # │ └── 'morphology_mip': DataTree[cyx] (1, 33131, 48358), (1, 16565, 24179), (1, 8282, 12089), (1, 4141, 6044), (1, 2070, 3022) | ||
| # ├── Labels | ||
| # │ ├── 'cell_labels': DataTree[yx] (33131, 48358), (16565, 24179), (8282, 12089), (4141, 6044), (2070, 3022) | ||
| # │ └── 'nucleus_labels': DataTree[yx] (33131, 48358), (16565, 24179), (8282, 12089), (4141, 6044), (2070, 3022) | ||
| # ├── Points | ||
| # │ └── 'transcripts': DataFrame with shape: (<Delayed>, 8) (3D points) | ||
| # ├── Shapes | ||
| # │ ├── 'cell_boundaries': GeoDataFrame shape: (162033, 1) (2D shapes) | ||
| # │ ├── 'cell_circles': GeoDataFrame shape: (162033, 2) (2D shapes) | ||
| # │ └── 'nucleus_boundaries': GeoDataFrame shape: (162033, 1) (2D shapes) | ||
| # └── Tables | ||
| # └── 'table': AnnData (162033, 248) | ||
| # with coordinate systems: | ||
| # ▸ 'global', with elements: | ||
| # morphology_focus (Images), morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) | ||
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| sdata2_kwargs = {} | ||
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| # process images | ||
| sdata2_kwargs["images"] = { | ||
| # morphology_focus or morphology_mip? | ||
| replicate_id + "_image": sdata.images["morphology_focus"], | ||
| } | ||
| sdata2_kwargs["labels"] = { | ||
| replicate_id + "_cell": sdata.labels["cell_labels"], | ||
| replicate_id + "_nucleus": sdata.labels["nucleus_labels"], | ||
| } | ||
| sdata2_kwargs["points"] = { | ||
| replicate_id + "_transcripts": sdata.points["transcripts"], | ||
| } | ||
| sdata2_kwargs["shapes"] = { | ||
| replicate_id + "_cell_boundaries": sdata.shapes["cell_boundaries"], | ||
| replicate_id + "_nucleus_boundaries": sdata.shapes["nucleus_boundaries"], | ||
| } | ||
| sdata2_kwargs["tables"] = { | ||
| replicate_id + "_cell_table": sdata.tables["table"], | ||
| } | ||
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| sdata2 = sd.SpatialData(**sdata2_kwargs) | ||
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| # TODO: | ||
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| sdatas.append(sdata2) | ||
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| sdata = sd.concatenate(sdatas) | ||
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| print(sdata) | ||
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| print("writing the data... ", end="", flush=True) | ||
| if os.path.exists(par["output"]): | ||
| shutil.rmtree(par["output"]) | ||
| sdata.write(par["output"]) |