Dev

CHANGELOG.md

@@ -1,3 +1,16 @@
+## [1.0.6] - 2024-03-13
+
+### Added
+- Spatial join between shapes (`from sdata.spatial import sjoin`)
+- H&E tutorial (basic usage)
+- New backend for the MERSCOPE reader (requires `rioxarray`, currently experimental, should use less RAM)
+
+## Changed
+- Using `MultiscaleSpatialImage` by default for multiplex imaging technologies
+
+## Fixed
+- Issue in report creation when channel names are integers
+
 ## [1.0.5] - 2024-03-01
 
 ### Changed

docs/api/spatial.md

@@ -1,3 +1,7 @@
+::: sopa.spatial.sjoin
+    options:
+      show_root_heading: true
+
 ::: sopa.spatial.mean_distance
     options:
       show_root_heading: true

mkdocs.yml

@@ -17,6 +17,7 @@ nav:
     - API usage: tutorials/api_usage.ipynb
     - Spatial statistics: tutorials/spatial.ipynb
     - Align images (e.g. H&E): tutorials/align.md
+    - H&E usage: tutorials/he.ipynb
     - Advanced segmentation: tutorials/advanced_segmentation.md
   - CLI: cli.md
   - API:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "sopa"
-version = "1.0.5"
+version = "1.0.6"
 description = "Spatial-omics pipeline and analysis"
 documentation = "https://gustaveroussy.github.io/sopa"
 homepage = "https://gustaveroussy.github.io/sopa"

sopa/embedding/patches.py

@@ -53,7 +53,7 @@ def _get_extraction_parameters(
         level = 0
 
     if magnification is None:
-        return level, 1, patch_width, True
+        return level, 1, patch_width * 2**level, True  # TODO: what if scaling != 2?
 
     if tiff_metadata["properties"].get("tiffslide.objective-power"):
         objective_power = int(tiff_metadata["properties"].get("tiffslide.objective-power"))

sopa/io/__init__.py

@@ -1,5 +1,5 @@
 from .imaging import macsima, phenocycler, hyperion, ome_tif
-from .explorer import write
+from .explorer import write, align
 from .standardize import write_standardized
 from .transcriptomics import merscope, xenium, cosmx
 from .histopathology import wsi, wsi_autoscale

sopa/io/explorer/images.py

@@ -10,13 +10,11 @@
 from multiscale_spatial_image import MultiscaleSpatialImage, to_multiscale
 from spatial_image import SpatialImage
 from spatialdata import SpatialData
-from spatialdata.models import Image2DModel
-from spatialdata.transformations import Affine
+from spatialdata.transformations import Affine, set_transformation
 from tqdm import tqdm
 
 from ..._sdata import get_intrinsic_cs, get_spatial_image
 from ...utils.image import resize_numpy, scale_dtype
-from ..imaging import _default_image_models_kwargs
 from ._constants import ExplorerConstants, FileNames, image_metadata
 from .utils import explorer_file_path
 
@@ -201,7 +199,6 @@ def align(
     image: SpatialImage,
     transformation_matrix_path: str,
     image_key: str = None,
-    image_models_kwargs: dict | None = None,
     overwrite: bool = False,
 ):
     """Add an image to the `SpatialData` object after alignment with the Xenium Explorer.
@@ -211,11 +208,9 @@ def align(
         image: A `SpatialImage` object. Note that `image.name` is used as the key for the aligned image.
         transformation_matrix_path: Path to the `.csv` transformation matrix exported from the Xenium Explorer
         image_key: Optional name of the image on which it has been aligned. Required if multiple images in the `SpatialData` object.
-        image_models_kwargs: Kwargs to the `Image2DModel` model.
         overwrite: Whether to overwrite the image, if already existing.
     """
     image_name = image.name
-    image_models_kwargs = _default_image_models_kwargs(image_models_kwargs)
 
     to_pixel = Affine(
         np.genfromtxt(transformation_matrix_path, delimiter=","),
@@ -226,13 +221,7 @@ def align(
     default_image = get_spatial_image(sdata, image_key)
     pixel_cs = get_intrinsic_cs(sdata, default_image)
 
-    image = Image2DModel.parse(
-        image,
-        dims=("c", "y", "x"),
-        transformations={pixel_cs: to_pixel},
-        c_coords=image.coords["c"].values,
-        **image_models_kwargs,
-    )
+    set_transformation(image, to_pixel, pixel_cs)
 
     log.info(f"Adding image {image_name}:\n{image}")
     sdata.add_image(image_name, image, overwrite=overwrite)

sopa/io/histopathology.py

@@ -11,12 +11,15 @@
 from spatialdata.transformations import Identity, Scale
 
 
-def wsi(path: str | Path, chunks: tuple[int, int, int] = (3, 256, 256)) -> SpatialData:
+def wsi(
+    path: str | Path, chunks: tuple[int, int, int] = (3, 256, 256), as_image: bool = False
+) -> SpatialData:
     """Read a WSI into a `SpatialData` object
 
     Args:
         path: Path to the WSI
         chunks: Tuple representing the chunksize for the dimensions `(C, Y, X)`.
+        as_image: If `True`, returns a, image instead of a `SpatialData` object
 
     Returns:
         A `SpatialData` object with a multiscale 2D-image of shape `(C, Y, X)`
@@ -46,6 +49,10 @@ def wsi(path: str | Path, chunks: tuple[int, int, int] = (3, 256, 256)) -> Spati
 
     multiscale_image = MultiscaleSpatialImage.from_dict(images)
     multiscale_image.attrs["metadata"] = tiff_metadata
+    multiscale_image.name = image_name
+
+    if as_image:
+        return multiscale_image
 
     return SpatialData(images={image_name: multiscale_image})
 

sopa/io/imaging.py

@@ -46,12 +46,15 @@ def _get_channel_names_macsima(files):
     return _deduplicate_names(df_antibodies)
 
 
-def _default_image_models_kwargs(image_models_kwargs: dict | None):
+def _default_image_models_kwargs(image_models_kwargs: dict | None = None):
     image_models_kwargs = {} if image_models_kwargs is None else image_models_kwargs
 
     if "chunks" not in image_models_kwargs:
         image_models_kwargs["chunks"] = (1, 1024, 1024)
 
+    if "scale_factors" not in image_models_kwargs:
+        image_models_kwargs["scale_factors"] = [2, 2, 2, 2]
+
     return image_models_kwargs
 
 
@@ -268,7 +271,7 @@ def ome_tif(path: Path, as_image: bool = False) -> SpatialImage | SpatialData:
     Returns:
         A `SpatialImage` or a `SpatialData` object
     """
-    image_models_kwargs = _default_image_models_kwargs(None)
+    image_models_kwargs = _default_image_models_kwargs()
     image_name = Path(path).absolute().name.split(".")[0]
     image: da.Array = imread(path)
 

sopa/io/report/generate.py

@@ -118,7 +118,7 @@ def channel_section(self):
             SubSection(
                 "Names",
                 Paragraph(
-                    f"Channels names:<br>{Message(', '.join(list(image.coords['c'].values)))}"
+                    f"Channels names:<br>{Message(', '.join(map(str, list(image.coords['c'].values))))}"
                 ),
             )
         ]