alterlab-squidpy-spatial

name	alterlab-squidpy-spatial
description	Analyzes spatial transcriptomics with squidpy (1.8.x) on AnnData and SpatialData objects, routing platforms correctly: Visium spots use spatial_neighbors(coord_type='grid') and pair with deconvolution, while Xenium/MERFISH single-cell data use coord_type='generic'/Delaunay neighbors and spatialdata-io readers (xenium, visium_hd, merscope). Runs sq.gr.spatial_neighbors, nhood_enrichment, co_occurrence, spatial_autocorr (Moran's I for spatially variable genes), ripley, and ligrec. Use when the user wants spatial transcriptomics, squidpy, Visium/Xenium/MERFISH analysis, neighborhood enrichment, co-occurrence, or spatially variable genes; QC/clustering uses alterlab-scanpy and spot deconvolution (destVI/Tangram) uses alterlab-scvi-tools. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Self-contained — runs under `uv run python` with squidpy (1.8.x, needs spatialdata>=0.7.1, scanpy>=1.9.3, anndata>=0.9, Python>=3.11) installed; no API key or account required.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

Squidpy: Spatial Transcriptomics

Squidpy is the scverse toolkit for spatially-resolved omics, built on AnnData and SpatialData. It answers questions a non-spatial scRNA-seq pipeline cannot: which cell types sit next to which (neighborhood enrichment), how cell-type pairs co-occur across distance (co-occurrence), which genes vary across tissue space (Moran's I / spatially variable genes), and what ligand-receptor signalling is plausible (ligrec). This skill does the spatial analysis; it hands non-spatial QC/clustering to alterlab-scanpy and spot deconvolution to alterlab-scvi-tools.

When to Use This Skill

Use when the request involves:

• Spatial transcriptomics / spatially-resolved omics on Visium, Visium HD, Xenium, MERFISH/MERSCOPE, or CosMx data.
• Building a spatial neighbor graph and running neighborhood enrichment, co-occurrence, interaction matrix, Ripley's statistics, or centrality scores.
• Finding spatially variable genes via Moran's I (spatial_autocorr) or Sepal.
• Ligand-receptor analysis in a spatial context (ligrec).
• Reading platform output into AnnData/SpatialData and choosing the right coord_type for the platform.

Does NOT Trigger

Request	Route to
Non-spatial scRNA-seq QC, normalization, PCA/UMAP, Leiden clustering, marker genes	alterlab-scanpy
Spot deconvolution / mapping cell types to Visium spots (destVI, Tangram), probabilistic batch correction/integration	alterlab-scvi-tools (see its references/models-spatial.md)
Building/slicing/concatenating .h5ad AnnData objects, layer & obsm wrangling (no spatial analysis)	alterlab-anndata
RNA-velocity / trajectory dynamics	alterlab-scvelo
Bulk RNA-seq differential expression from a count matrix	alterlab-pydeseq2
Raw FASTQ → expression matrix (read alignment/quantification)