Add tiling QC metric for tile-boundary segmentation artifacts by timtreis · Pull Request #1157 · scverse/squidpy

timtreis · 2026-04-10T22:11:50Z

Summary

Adds sq.experimental.tl.calculate_tiling_qc — per-cell scoring that detects cells artificially cut by tile boundaries during segmentation, using collinearity-based straight-edge detection on contours
Adds sq.experimental.pl.tiling_qc — diagnostic plot via spatialdata-plot where the tile grid emerges from high-scoring cells without requiring tile-border metadata
Includes cell-aware tiling infrastructure (_tiling.py) for scalable labels-only tile extraction — will be shared with [EXPERIMENTAL]: Integrate cp-measure #982 when merged
Scores stored in .obs of a QC AnnData table ({labels_key}_qc) with proper spatialdata_attrs linking and algorithm params in .uns["tiling_qc"]

Metrics

Metric	Description
`max_straight_edge_ratio`	Longest collinear boundary segment / equivalent diameter
`cardinal_alignment_score`	Axis-alignment of that segment (1 = cardinal, 0 = diagonal)
`cut_score`	Product of the two

Cells segmented in tiles get cut at tile borders, producing fragments with artificially straight edges. This adds: - `sq.experimental.tl.calculate_tiling_qc`: per-cell scoring via collinearity-based straight-edge detection (max_straight_edge_ratio, cardinal_alignment_score, cut_score). Scores stored in .obs of a QC AnnData table linked to the labels element via spatialdata_attrs. Algorithm parameters recorded in .uns["tiling_qc"]. - `sq.experimental.pl.tiling_qc`: diagnostic plot via spatialdata-plot (renders labels coloured by score; tile grid emerges from the data). - Cell-aware tiling infrastructure (_tiling.py) for scalable labels-only tile extraction without materialising full arrays. - Test fixture with 400x400 dask-backed ellipsoid cells cut by a 3x3 tile grid, with ground-truth cut/intact classification. - 35 tests (unit, integration, visual regression). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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- Bump fixture from 40 cells on 400x400 to 120 cells on 600x600 for more visible tile-grid pattern in diagnostic plots - Pin spatialdata-plot>=0.3.3 for correct continuous color rendering - Regenerate visual reference images - Use _IMAGE_SIZE constant in centroid bounds test Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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codecov · 2026-04-10T22:34:43Z

Codecov Report

❌ Patch coverage is 56.84455% with 186 lines in your changes missing coverage. Please review.
✅ Project coverage is 72.57%. Comparing base (373228d) to head (1ccb020).

Files with missing lines	Patch %	Lines
src/squidpy/experimental/im/_tiling.py	43.67%	83 Missing and 6 partials ⚠️
src/squidpy/experimental/tl/_tiling_qc.py	64.40%	72 Missing and 17 partials ⚠️
src/squidpy/experimental/pl/_tiling_qc.py	62.50%	3 Missing and 3 partials ⚠️
src/squidpy/_utils.py	71.42%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1157      +/-   ##
==========================================
- Coverage   73.56%   72.57%   -0.99%     
==========================================
  Files          44       47       +3     
  Lines        6929     7359     +430     
  Branches     1174     1246      +72     
==========================================
+ Hits         5097     5341     +244     
- Misses       1347     1507     +160     
- Partials      485      511      +26

Files with missing lines	Coverage Δ
src/squidpy/_utils.py	`57.51% <71.42%> (+0.29%)`	⬆️
src/squidpy/experimental/pl/_tiling_qc.py	`62.50% <62.50%> (ø)`
src/squidpy/experimental/im/_tiling.py	`43.67% <43.67%> (ø)`
src/squidpy/experimental/tl/_tiling_qc.py	`64.40% <64.40%> (ø)`

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- JIT-compile the two-pointer collinearity scan with @njit for ~10-50x speedup on the per-cell hot path - Cap contour points at 500 via arc-length resampling to bound O(n²) - Handle contour closure: scan 3 rotations so straight segments crossing the start/end junction are not split - Vectorise _resample_contour with np.searchsorted (no Python loops) - Replace _zero_non_owned loop with single np.isin pass - Add tqdm progress bar that tracks cells (not tiles), updates on completion for correct parallel reporting - Extract _SCORE_COLUMNS / _NAN_SCORES constants to deduplicate - Precompute segment lengths once across rotations Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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Drop 29 tests that over-tested private internals. Keep 4 behavioural tests (output structure, metric discrimination, tiling invariant, error handling) and 3 visual regression tests (one per score column). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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- Replace joblib parallelisation with dask.delayed + dask.compute for native integration with dask-backed zarr data. Tiles are scheduled as delayed tasks; the dask scheduler handles chunk caching and worker management. - Add n_jobs parameter (default -1 = all CPUs) using a threaded scheduler, and an optional dask.distributed.Client parameter for cluster execution. Warn via logger when both are specified. - Add affinity-aware cpu_count() to squidpy/_utils.py that respects cgroup limits (SLURM, Docker, taskset) via os.sched_getaffinity, replacing multiprocessing.cpu_count throughout the codebase. - Fix NameError in pl.tiling_qc (**kwargs referenced after removal), keep spatialdata_plot import lazy, remove unused typing imports. - Replace assert with raise ValueError in verify_coverage. - Add nogil=True to numba collinearity scan for thread parallelism. - Use public API (sq.experimental.tl/pl) in tests. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

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