WIP: kpmp spatialdata object

Author: keller-mark

docs/notebooks/spatial_data_kpmp.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Vitessce Widget Tutorial
+
+# ## Import dependencies
+# 
+
+# https://github.com/vitessce/vitessce/blob/main/examples/configs/src/view-configs/spatial-beta/kpmp.js
+
+# In[2]:
+
+
+from os.path import join
+
+
+# In[1]:
+
+
+import spatialdata
+from spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, TableModel
+from spatialdata.transformations import (
+    Affine,
+    MapAxis,
+    Scale,
+    Sequence,
+    Translation,
+    get_transformation,
+    set_transformation,
+)
+from anndata import read_zarr, AnnData
+import tifffile
+from tifffile import imread, TiffFile
+import xml.etree.ElementTree
+import io
+import zarr
+
+
+
+# In[2]:
+
+
+
+
+# In[3]:
+
+
+base_dir = join("..", "..", "..", "kpmp-f2f-march-2023", "S-1905-017737")
+
+
+# In[ ]:
+
+
+img_path = join(base_dir, "S-1905-017737_PAS_2of2_bf.ome.tif")
+seg_path = join(base_dir, "S-1905-017737_PAS_2of2.ome.tif")
+
+adata_paths = {
+    "cortical_interstitia": join(base_dir, "Cortical Interstitium.adata.zarr"),
+    "non_globally_sclerotic_glomeruli": join(base_dir, "Glomeruli.adata.zarr"),
+    "globally_sclerotic_glomeruli": join(base_dir, "Globally Sclerotic Glomeruli.adata.zarr"),
+    "tubules": join(base_dir, "Tubules with Area non infinity.adata.zarr"),
+    "arteries_arterioles": None,
+    "interstitialfibrosis_and_tubular_atrophy": join(base_dir, "IFTA.adata.zarr"),
+    "peritubular_capillaries": join(base_dir, "Peritubular Capillaries renamed.adata.zarr"),
+}
+
+
+# In[ ]:
+
+
+# The shape of the data should be c(z)yx for 2D (3D) images
+
+
+# In[ ]:
+
+
+img_arr = imread(img_path)
+
+
+# In[ ]:
+
+
+seg_store = imread(seg_path, aszarr=True)
+seg_z = zarr.open(seg_store)
+
+
+# In[ ]:
+
+
+def clean_adata(adata):
+    colnames = adata.obs.columns.tolist()
+    adata.obs = adata.obs.rename(columns=dict([ (c, c.replace(" ", "_")) for c in colnames ]))
+    return adata
+
+
+# In[ ]:
+
+
+sdata = SpatialData(
+    images={
+        "image": Image2DModel.parse(img_arr, scale_factors=[2, 2, 2, 2, 2]),
+    },
+    tables={
+        f"table_{obs_type}": TableModel.parse(clean_adata(read_zarr(adata_path)))
+        for obs_type, adata_path in adata_paths.items()
+        if adata_path is not None
+    }
+)
+
+for i, obs_type in enumerate(adata_paths.keys()):
+    sdata[f"labels_{obs_type}"] = Labels2DModel.parse(seg_z['1'][i, :, :], scale_factors=[2, 2, 2, 2])
+    # Scale by a factor of 2 since we are using the second resolution from the original file.
+    scale = Scale([2.0, 2.0], axes=("y","x"))
+    set_transformation(sdata[f"labels_{obs_type}"], scale, to_coordinate_system="global")
+
+
+# In[ ]:
+
+
+sdata.write(join(base_dir, "sdata.zarr"), overwrite=True)
+print(join(base_dir, "sdata.zarr"))
+print("Done")
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+