Add tile-cut stitching follow-up to calculate_tiling_qc

src/squidpy/experimental/im/__init__.py

@@ -9,12 +9,14 @@
 from ._make_tiles import make_tiles, make_tiles_from_spots
 from ._qc_image import qc_image
 from ._qc_metrics import QCMetric
+from ._stitched_labels import make_stitched_labels
 
 __all__ = [
     "BackgroundDetectionParams",
     "FelzenszwalbParams",
     "QCMetric",
     "WekaParams",
+    "make_stitched_labels",
     "detect_tissue",
     "make_tiles",
     "make_tiles_from_spots",

src/squidpy/experimental/tl/__init__.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
 from ._tiling_qc import calculate_tiling_qc
+from ._tiling_stitch import stitch_tile_cuts
 
-__all__ = ["calculate_tiling_qc"]
+__all__ = ["calculate_tiling_qc", "stitch_tile_cuts"]

src/squidpy/experimental/tl/_tiling_qc.py

@@ -56,6 +56,24 @@
 
 __all__ = ["calculate_tiling_qc"]
 
+
+def resolve_labels_array(sdata: sd.SpatialData, labels_key: str, scale: str | None) -> xr.DataArray:
+    """Resolve a labels element to its 2-D :class:`~xarray.DataArray`.
+
+    Single-scale elements pass through; multi-scale (:class:`xarray.DataTree`)
+    elements require a ``scale`` selector.
+
+    Shared by :func:`calculate_tiling_qc` and the downstream stitch helpers
+    so the multi-scale branch stays consistent.
+    """
+    node = sdata.labels[labels_key]
+    if isinstance(node, xr.DataTree):
+        if scale is None:
+            raise ValueError(f"Labels '{labels_key}' is multi-scale; pass `scale` (e.g. 'scale0').")
+        return node[scale].ds["image"]
+    return node
+
+
 # Minimum cell area in pixels - smaller cells produce noisy contours
 _MIN_CELL_AREA = 20
 
@@ -531,13 +549,7 @@ def calculate_tiling_qc(
     if n_neighbors < 1:
         raise ValueError(f"n_neighbors must be >= 1, got {n_neighbors}.")
 
-    labels_node = sdata.labels[labels_key]
-    if isinstance(labels_node, xr.DataTree):
-        if scale is None:
-            raise ValueError("When using multi-scale labels, please specify the scale.")
-        labels_da = labels_node[scale].ds["image"]
-    else:
-        labels_da = labels_node
+    labels_da = resolve_labels_array(sdata, labels_key, scale)
 
     cell_info = _compute_centroids_for_labels(sdata, labels_key, labels_da, scale)
     if not cell_info:
@@ -663,6 +675,21 @@ def _process_one(spec):
 
     if inplace:
         table_key = adata_key_added if adata_key_added is not None else f"{labels_key}_qc"
+        # If a previous QC table at this key carried stitch results, those are
+        # stale relative to the new outlier set.  The new AnnData doesn't
+        # include them; warn the user so they know to re-run stitch_tile_cuts.
+        if table_key in sdata.tables:
+            existing = sdata.tables[table_key]
+            stitch_cols = [
+                c
+                for c in ("stitch_group_id", "is_stitched", "n_pieces", "stitch_confidence")
+                if c in existing.obs.columns
+            ]
+            if stitch_cols:
+                logg.warning(
+                    f"Previous QC table at '{table_key}' carried stitch columns "
+                    f"({stitch_cols}); re-run squidpy.experimental.tl.stitch_tile_cuts."
+                )
         sdata.tables[table_key] = TableModel.parse(adata)
         return None
     return adata