---

name: tools-workflows description: "Use Squidpy tool-layer workflows for sliding windows, distance-to-anchor design matrices, and plotting handoffs." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Tools Workflows

Use this sub-skill when a Squidpy task needs squidpy.tl helper workflows for spatial windows or distance-to-anchor design matrices. Keep plotting details routed to visualization once the design matrix exists.

Use This For

• Assigning observations to spatial windows with sq.tl.sliding_window.
• Building normalized distance-to-anchor design matrices with sq.tl.var_by_distance.
• Preserving library, donor, batch, or other covariates in design matrices for downstream models and plots.
• Handing a valid design matrix to sq.pl.var_by_distance without duplicating plot-formatting guidance.

Route Elsewhere

• Use datasets-and-io for AnnData/SpatialData structure, coordinate validation, readers, and missing metadata repair.
• Use graph-analysis for spatial neighbor graphs, graph statistics, ligand-receptor, Ripley, Moran/Geary, Sepal, niche, or graph masking workflows.
• Use visualization for sq.pl.var_by_distance layout, palettes, axes, saving, or other plot-formatting choices after adata.obsm[design_matrix_key] is valid.
• Use image-analysis or experimental-imaging for image containers, segmentation, tissue tiling, QC, staining, or SpatialData image workflows.

Start Here

1. Read references/tools-workflows.md for workflow recipes and output checks.
2. Read references/api-reference.md for focused signatures, parameters, and storage keys.
3. Read references/troubleshooting.md for coordinate columns, anchors, libraries, covariates, partial windows, and plot handoff failures.
4. Run scripts/var_by_distance_smoke.py --help to inspect the bundled no-download smoke check, then run it with --quiet for a compact installation/API check.

Minimal Pattern

import squidpy as sq

sq.tl.sliding_window(
    adata,
    window_size=100,
    overlap=0,
    coord_columns=("globalX", "globalY"),
    sliding_window_key="sliding_window_assignment",
)

sq.tl.var_by_distance(
    adata,
    groups="Tumor",
    cluster_key="cell_type",
    library_key="sample",
    covariates="donor",
    design_matrix_key="design_matrix",
)

sq.pl.var_by_distance(
    adata,
    var="GeneA",
    anchor_key="Tumor",
    design_matrix_key="design_matrix",
    covariate="donor",
)

Confirm storage keys before downstream use: sliding_window writes columns to adata.obs when copy=False, and var_by_distance writes a pandas design matrix to adata.obsm[design_matrix_key] when copy=False.