can take the min/max in channels aggregation (#147)

docs/api/aggregation.md

@@ -1,3 +1,12 @@
 # Aggregation
 
+!!! tips "Recommendation"
+    We recommend using the `sopa.aggregate` function below, which is a wrapper for all types of aggregation. Internally, it uses `aggregate_channels`, `count_transcripts`, and/or `aggregate_bins`, which are also documented below if needed.
+
 ::: sopa.aggregate
+
+::: sopa.aggregation.aggregate_channels
+
+::: sopa.aggregation.count_transcripts
+
+::: sopa.aggregation.aggregate_bins

sopa/aggregation/__init__.py

@@ -1,5 +1,5 @@
 from .bins import aggregate_bins
-from .channels import average_channels
+from .channels import average_channels, aggregate_channels
 from .transcripts import count_transcripts
 from .aggregation import aggregate, Aggregator
 from .overlay import overlay_segmentation

sopa/aggregation/aggregation.py

@@ -17,7 +17,9 @@
     get_spatial_element,
     get_spatial_image,
 )
-from . import aggregate_bins, average_channels, count_transcripts
+from . import aggregate_bins
+from . import aggregate_channels as _aggregate_channels
+from . import count_transcripts
 
 log = logging.getLogger(__name__)
 
@@ -157,7 +159,7 @@ def compute_table(
                 self.filter_cells(self.table.X.sum(axis=1) < min_transcripts)
 
         if aggregate_channels:
-            mean_intensities = average_channels(
+            mean_intensities = _aggregate_channels(
                 self.sdata,
                 image_key=self.image_key,
                 shapes_key=self.shapes_key,

sopa/aggregation/channels.py

@@ -5,6 +5,7 @@
 import dask
 import geopandas as gpd
 import numpy as np
+import numpy.ma as ma
 import shapely
 from dask.diagnostics import ProgressBar
 from shapely.geometry import Polygon, box
@@ -16,35 +17,47 @@
 
 log = logging.getLogger(__name__)
 
+AVAILABLE_MODES = ["average", "min", "max"]
+
 
 def average_channels(
+    sdata: SpatialData, image_key: str = None, shapes_key: str = None, expand_radius_ratio: float = 0
+) -> np.ndarray:
+    log.warning("average_channels is deprecated, use `aggregate_channels` instead")
+    return aggregate_channels(sdata, image_key, shapes_key, expand_radius_ratio, mode="average")
+
+
+def aggregate_channels(
     sdata: SpatialData,
     image_key: str = None,
     shapes_key: str = None,
     expand_radius_ratio: float = 0,
+    mode: str = "average",
 ) -> np.ndarray:
-    """Average channel intensities per cell.
+    """Aggregate the channel intensities per cell (either `"average"`, or take the `"min"` / `"max"`).
 
     Args:
         sdata: A `SpatialData` object
         image_key: Key of `sdata` containing the image. If only one `images` element, this does not have to be provided.
         shapes_key: Key of `sdata` containing the cell boundaries. If only one `shapes` element, this does not have to be provided.
         expand_radius_ratio: Cells polygons will be expanded by `expand_radius_ratio * mean_radius`. This help better aggregate boundary stainings.
+        mode: Aggregation mode. One of `"average"`, `"min"`, `"max"`. By default, average the intensity inside the cell mask.
 
     Returns:
         A numpy `ndarray` of shape `(n_cells, n_channels)`
     """
+    assert mode in AVAILABLE_MODES, f"Invalid {mode=}. Available modes are {AVAILABLE_MODES}"
+
     image = get_spatial_image(sdata, image_key)
 
     geo_df = get_boundaries(sdata, key=shapes_key)
     geo_df = to_intrinsic(sdata, geo_df, image)
     geo_df = expand_radius(geo_df, expand_radius_ratio)
 
-    log.info(f"Averaging channels intensity over {len(geo_df)} cells with expansion {expand_radius_ratio=}")
-    return _average_channels_aligned(image, geo_df)
+    return _aggregate_channels_aligned(image, geo_df, mode)
 
 
-def _average_channels_aligned(image: DataArray, geo_df: gpd.GeoDataFrame | list[Polygon]) -> np.ndarray:
+def _aggregate_channels_aligned(image: DataArray, geo_df: gpd.GeoDataFrame | list[Polygon], mode: str) -> np.ndarray:
     """Average channel intensities per cell. The image and cells have to be aligned, i.e. be on the same coordinate system.
 
     Args:
@@ -54,11 +67,17 @@ def _average_channels_aligned(image: DataArray, geo_df: gpd.GeoDataFrame | list[
     Returns:
         A numpy `ndarray` of shape `(n_cells, n_channels)`
     """
+    log.info(f"Aggregating channels intensity over {len(geo_df)} cells with {mode=}")
+
     cells = geo_df if isinstance(geo_df, list) else list(geo_df.geometry)
     tree = shapely.STRtree(cells)
 
-    intensities = np.zeros((len(cells), len(image.coords["c"])))
+    n_channels = len(image.coords["c"])
     areas = np.zeros(len(cells))
+    if mode == "min":
+        aggregation = np.full((len(cells), n_channels), fill_value=np.inf)
+    else:
+        aggregation = np.zeros((len(cells), n_channels))
 
     chunk_sizes = image.data.chunks
     offsets_y = np.cumsum(np.pad(chunk_sizes[1], (1, 0), "constant"))
@@ -86,9 +105,20 @@ def _average_chunk_inside_cells(chunk, iy, ix):
 
             mask = rasterize(cell, sub_image.shape[1:], bounds)
 
-            intensities[index] += np.sum(sub_image * mask, axis=(1, 2))
             areas[index] += np.sum(mask)
 
+            if mode == "min":
+                masked_image = ma.masked_array(sub_image, 1 - np.repeat(mask[None], n_channels, axis=0))
+                aggregation[index] = np.minimum(aggregation[index], masked_image.min(axis=(1, 2)))
+            elif mode in ["average", "max"]:
+                func = np.sum if mode == "average" else np.max
+                values = func(sub_image * mask, axis=(1, 2))
+
+                if mode == "average":
+                    aggregation[index] += values
+                else:
+                    aggregation[index] = np.maximum(aggregation[index], values)
+
     with ProgressBar():
         tasks = [
             dask.delayed(_average_chunk_inside_cells)(chunk, iy, ix)
@@ -97,4 +127,7 @@ def _average_chunk_inside_cells(chunk, iy, ix):
         ]
         dask.compute(tasks)
 
-    return intensities / areas[:, None].clip(1)
+    if mode == "average":
+        return aggregation / areas[:, None].clip(1)
+    else:
+        return aggregation

tests/test_aggregation.py

@@ -6,14 +6,14 @@
 import xarray as xr
 from shapely.geometry import Polygon, box
 
-from sopa.aggregation.channels import _average_channels_aligned
+from sopa.aggregation.channels import _aggregate_channels_aligned
 from sopa.aggregation.transcripts import _count_transcripts_aligned
 
 dask.config.set({"dataframe.query-planning": False})
 import dask.dataframe as dd  # noqa
 
 
-def test_average_channels_aligned():
+def test_aggregate_channels_aligned():
     image = np.random.randint(1, 10, size=(3, 8, 16))
     arr = da.from_array(image, chunks=(1, 8, 8))
     xarr = xr.DataArray(arr, dims=["c", "y", "x"])
@@ -24,11 +24,23 @@ def test_average_channels_aligned():
     # One cell is on the first block, one is overlapping on both blocks, and one is on the last block
     cells = [box(x, y, x + cell_size - 1, y + cell_size - 1) for x, y in cell_start]
 
-    means = _average_channels_aligned(xarr, cells)
+    mean_intensities = _aggregate_channels_aligned(xarr, cells, "average")
+    min_intensities = _aggregate_channels_aligned(xarr, cells, "min")
+    max_intensities = _aggregate_channels_aligned(xarr, cells, "max")
 
-    true_means = np.stack([image[:, y : y + cell_size, x : x + cell_size].mean(axis=(1, 2)) for x, y in cell_start])
+    true_mean_intensities = np.stack(
+        [image[:, y : y + cell_size, x : x + cell_size].mean(axis=(1, 2)) for x, y in cell_start]
+    )
+    true_min_intensities = np.stack(
+        [image[:, y : y + cell_size, x : x + cell_size].min(axis=(1, 2)) for x, y in cell_start]
+    )
+    true_max_intensities = np.stack(
+        [image[:, y : y + cell_size, x : x + cell_size].max(axis=(1, 2)) for x, y in cell_start]
+    )
 
-    assert (means == true_means).all()
+    assert (mean_intensities == true_mean_intensities).all()
+    assert (min_intensities == true_min_intensities).all()
+    assert (max_intensities == true_max_intensities).all()
 
 
 def test_count_transcripts():