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name: spatial-integration description: "Use for OmicVerse spatial transcriptomics, histology-to-spatial prediction, deconvolution, cell mapping, tissue zones, spatial IO, and spatial-adjacent multimodal integration." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Spatial Integration

Use this sub-skill when the user needs OmicVerse help with spatial transcriptomics data, spatial readers, histology-to-spatial routes, spatial deconvolution or mapping, spatially variable genes, tissue zones, spatial communication, bulk/single/spatial integration, or epigenomics workflows that feed spatial interpretation.

Start at the root routing skill if the task may belong to another OmicVerse domain.

Route Here

• Read or validate spatial inputs: 10x Visium/Visium HD, Xenium, NanoString/CosMx, Atera, or AnnData objects with obsm['spatial'].
• Build spatial neighborhoods, compute Moran/Geary statistics, select spatially variable genes, crop/rotate/map spatial images, or plot spatial coordinates/segmentations.
• Map scRNA-seq references to spatial spots or cells with Tangram, Deconvolution, CellMap, CellLoc, Single2Spatial, or related cell-composition helpers.
• Derive tissue zones from cell-abundance matrices, run cell-type split purification/balancing, or spatialize communication results.
• Gate optional heavy routes: pySTAGATE, pySTAligner, pySpaceFlow, CAST, cellcharter, STT, GASTON, cell2location/RCTD, and ov.space.histo.
• Use ov.epi or ov.bulk2single when ATAC/multiome or bulk-to-single/spatial outputs become spatial inputs.

Route Elsewhere

• Generic AnnData reading, QC, normalization, PCA, neighbors, UMAP, or plotting basics: core analysis.
• Single-cell annotation, marker ranking, batch integration, trajectory, or communication before spatial projection: single-cell workflows.
• Bulk RNA-seq, enrichment, metabolomics, proteomics, or microbiome table statistics that do not become spatial maps: multiomics statistics.
• FASTQ alignment, Space Ranger execution, external binary pipelines, GWAS, AIRR, or molecular/docking work: specialist domains.

Safe First Step

Before running heavy spatial models, validate the file layout or AnnData slots:

python sub-skills/spatial-integration/scripts/check_spatial_inputs.py --kind auto --path PATH_TO_INPUT
python sub-skills/spatial-integration/scripts/check_spatial_inputs.py --kind h5ad --path spatial.h5ad
python sub-skills/spatial-integration/scripts/check_spatial_inputs.py --kind nanostring --path SAMPLE_DIR --counts-file exprMat.csv --meta-file metadata.csv

Expected success signal: ERRORS: 0. Warnings are common for optional images, boundaries, or caches and should be reviewed before plotting or segmentation.

Reference Map

• Use spatial workflows for end-to-end recipes and model gating.
• Use API reference for concrete OmicVerse functions, signatures, inputs, and outputs.
• Use data formats for Visium HD, Xenium, NanoString, and AnnData slot contracts.
• Use troubleshooting for missing images, coordinate mismatch, optional dependency, GPU/backend, and deconvolution reference errors.

Operating Rules

• Never run histology prediction, cell2location, RCTD, Tangram training, CAST, or torch-geometric methods as a default smoke check; validate inputs first and ask for explicit runtime/backend choices.
• Prefer load_image=False or bounded image_max_dim for Xenium morphology and WSI-scale data until the user confirms memory budget.
• Confirm gene intersection between reference and spatial data before training mapping/deconvolution models; report the number of shared genes and dropped features.
• Keep obsm['spatial'], uns['spatial'], obs['geometry'], layers['counts'], and method-specific obsm outputs explicit in handoffs.
• Treat optional downloads, model weights, Hugging Face access, GPU use, and large tile caches as opt-in operations.