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.
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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.
Bulk RNA-seq differential expression from a count matrix
alterlab-pydeseq2
Raw FASTQ → expression matrix (read alignment/quantification)
alterlab-rnaseq-quant
Diversity / ecology statistics on a feature table
alterlab-scikit-bio
If the user wants the whole pipeline ("cluster my Xenium data, then find which
cell types are neighbors"), run the scanpy clustering step under alterlab-scanpy
first, then return here for the spatial graph and enrichment.
The One Decision That Matters: Platform → coord_type
Squidpy's spatial graph depends on the measurement geometry. Getting coord_type
wrong silently produces a meaningless graph. (All parameter behavior below is from
the squidpy 1.8 sq.gr.spatial_neighbors API.)
coord_type=None auto-picks "grid" only when spatial is in adata.uns with
n_neighs=6 (the Visium signature); otherwise it falls back to "generic". Set
coord_type explicitly rather than relying on auto-detection.
delaunay=True is only used when coord_type="generic"; it builds the graph from
a Delaunay triangulation instead of k-nearest spots. n_rings is only used for
coord_type="grid".
spatialdata-io reader names are verified against the spatialdata-io stable API.
Squidpy 1.8 accepts SpatialData objects directly; see
references/spatialdata_io.md for the SpatialData ↔ AnnData (table) flow.
Standard Spatial Workflow
QC, normalization, HVGs, PCA, neighbors, Leiden, and sc.tl.umap are scanpy
steps — run them via alterlab-scanpy. Once you have clusters / cell-type labels,
do the spatial part here.
import squidpy as sq
# 1. Build the spatial neighbor graph (choose coord_type per the table above)
sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True) # Xenium/MERFISH# sq.gr.spatial_neighbors(adata, coord_type="grid", n_neighs=6) # Visium# 2. Neighborhood enrichment: which cluster pairs are spatially adjacent?
sq.gr.nhood_enrichment(adata, cluster_key="leiden")
sq.pl.nhood_enrichment(adata, cluster_key="leiden")
# 3. Co-occurrence across distance
sq.gr.co_occurrence(adata, cluster_key="leiden")
sq.pl.co_occurrence(adata, cluster_key="leiden", clusters="0")
# 4. Spatially variable genes via Moran's I
sq.gr.spatial_autocorr(adata, mode="moran")
svgs = adata.uns["moranI"].head(20) # ranked by Moran's I# 5. Ligand-receptor interaction (Omnipath-backed)
sq.gr.ligrec(adata, cluster_key="leiden")
Other graph statistics: sq.gr.interaction_matrix, sq.gr.centrality_scores,
sq.gr.ripley (clustering/dispersion vs. CSR), and sq.gr.sepal (an alternative
spatially-variable-gene test). Visualize tissue with sq.pl.spatial_scatter
(spot/point) or sq.pl.spatial_segment (segmented cells). For image features on
H&E/IF, the sq.im module (process, segment, calculate_image_features)
operates on an ImageContainer.
Helper script — build the graph and run the core statistics in one call:
See scripts/spatial_neighborhood.py --help. It chooses coord_type from
--platform, runs spatial_neighbors, nhood_enrichment, co_occurrence, and
spatial_autocorr, and writes a JSON summary (top spatially variable genes + the
enrichment z-score matrix) plus the updated .h5ad.
Deeper References
references/platform_routing.md — full platform→coord_type decision table, the
n_neighs/n_rings/delaunay parameter semantics, and per-platform gotchas.
references/analysis_recipes.md — copy-paste recipes for each sq.gr / sq.pl
function with the parameters that matter and how to read the outputs.
references/spatialdata_io.md — reading Xenium / Visium HD / MERSCOPE into
SpatialData and getting the AnnData table squidpy operates on.
Self-Check Before Reporting
Did you set coord_type to match the platform (grid for Visium, generic for
single-cell)? A wrong graph invalidates every downstream statistic.
Did clustering/QC run under alterlab-scanpy (this skill assumes labels exist)?
For Visium spot data, did you flag that deconvolution (alterlab-scvi-tools)
is needed before cell-type-level claims — spots are multi-cell?
Are nhood_enrichment z-scores reported with the permutation context, not as raw
counts?
