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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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?
