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bio-spatial-transcriptomics-spatial-domains
Maintainer FreedomIntelligence · Last updated April 1, 2026
Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-domains
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Python: squidpy.gr.spatial_neighbors() → Leiden clustering with spatial graph, or BayesSpace/SpaGCN.
- Identify tissue domains in my spatial data" → Cluster spots/cells considering both gene expression and physical proximity to define anatomically coherent spatial domains. Python: squidpy.gr.spatial_neighbors() → Leiden clustering with spatial graph, or BayesSpace/SpaGCN.
- Identify spatial domains and tissue regions by combining expression and spatial information.
- sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30) sc.tl.leiden(adata, resolution=0.5, key_added='leiden').
Source Doc
Excerpt From SKILL.md
Standard Clustering (Expression Only)
Goal: Cluster spots based purely on gene expression, ignoring spatial location.
Approach: Build an expression-based neighbor graph, then apply Leiden community detection.
## Visualize on tissue
sq.pl.spatial_scatter(adata, color='leiden', size=1.3)
Spatial-Aware Clustering with Squidpy
Goal: Cluster spots using only spatial proximity to identify contiguous tissue regions.
Approach: Build a spatial neighbor graph, then run Leiden clustering on the spatial graph.
Use cases
- Use when identifying tissue domains or spatial regions.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- spatial-neighbors - Build spatial graphs (prerequisite)
- spatial-statistics - Compute spatial statistics per domain
- single-cell/clustering - Standard clustering methods
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