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name: squidpy description: "Use Squidpy for spatial omics data loading, graph statistics, image analysis, visualization, tool workflows, and experimental SpatialData imaging." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Squidpy

Use this repo skill when a task involves Squidpy, the scverse toolkit for spatial molecular and spatial single-cell analysis in Python. Squidpy builds on AnnData, Scanpy, SpatialData, scikit-image, and Matplotlib to load spatial data, build spatial neighbor graphs, compute spatial statistics, work with tissue images, and render spatial plots.

Start Here

1. Confirm Squidpy is importable with python -c "import squidpy as sq; print(sq.__version__)" or run scripts/squidpy_import_check.py --json.
2. Choose the nearest sub-skill from the route map below.
3. Use the linked sub-skill references for API signatures, required data layout, validation checks, and troubleshooting.
4. Keep workflows explicit: name spatial_key, library_id, table_key, image_key, labels_key, connectivity_key, and output keys when ambiguity is possible.

Route Map

• Use sub-skills/datasets-and-io/SKILL.md for squidpy.datasets, squidpy.read.*, .h5ad/SpatialData validation, Visium/Vizgen/Nanostring layouts, cache behavior, and download avoidance.
• Use sub-skills/graph-analysis/SKILL.md for squidpy.gr graph builders, spatial neighbor graphs, graph statistics, ligand-receptor analysis, Moran/Geary autocorrelation, Ripley statistics, Sepal, niches, and custom graph builders.
• Use sub-skills/image-analysis/SKILL.md for stable squidpy.im ImageContainer workflows, image processing, segmentation, and AnnData image-feature extraction.
• Use sub-skills/visualization/SKILL.md for squidpy.pl spatial overlays, segmentation plots, graph/statistic plots, ligand-receptor heatmaps, and variation-by-distance plots.
• Use sub-skills/tools-workflows/SKILL.md for squidpy.tl.sliding_window, squidpy.tl.var_by_distance, design-matrix outputs, and plotting handoffs.
• Use sub-skills/experimental-imaging/SKILL.md for squidpy.experimental SpatialData tissue detection, tiling, image QC, stain workflows, experimental feature extraction, tiling QC, stitching, and experimental plots.

Common Workflow Order

• Load or validate data first with datasets-and-io; graph, plotting, image, and experimental workflows all depend on correct coordinate/table/image keys.
• Build graph outputs with graph-analysis before using graph plot functions in visualization.
• Use stable image-analysis for ImageContainer tasks; use experimental-imaging only when the task is explicitly SpatialData-based or needs experimental tissue/tiling/stain/QC APIs.
• Use tools-workflows to create sliding-window assignments or distance-to-anchor design matrices, then route plot styling to visualization.

What To Read

• references/package-overview.md for public modules, object conventions, package requirements, and workflow ownership.
• references/troubleshooting.md for install/import issues, optional dependencies, dataset downloads, AnnData/SpatialData key mismatches, plotting backends, and deprecated napari-plugin guidance.
• references/repo-provenance.md for the repository snapshot and evidence paths used to generate this skill.
• references/repo-routing-metadata.json for structured managed-router scenario placement.

Safe Defaults

• Prefer explicit local paths and keys over global settings or inferred defaults.
• Treat sq.datasets.* as potentially networked; local sq.read.* and saved .h5ad/SpatialData reads are safer for offline work.
• Prefer mode-specific graph constructors such as sq.gr.spatial_neighbors_knn, sq.gr.spatial_neighbors_radius, sq.gr.spatial_neighbors_delaunay, and sq.gr.spatial_neighbors_grid; avoid new code built around deprecated sq.gr.spatial_neighbors.
• Use n_jobs=1, small permutation counts, preview=False, and noninteractive Matplotlib backends for smoke tests and automation.
• Route experimental APIs carefully; squidpy.experimental surfaces can change faster than stable sq.im, sq.gr, sq.tl, and sq.pl APIs.