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bio-spatial-transcriptomics-spatial-domains
维护者 FreedomIntelligence · 最近更新 2026年4月1日
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.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-domains
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Python：squidpy.gr.spatial_neighbors() → Leiden 聚类 ，支持 spatial graph，或 BayesSpace/SpaGCN。
- Identify tissue domains in my spatial data" → Cluster spots/cells considering both gene expression 、 physical proximity to define anatomically coherent spatial domains. Python：squidpy.gr.spatial_neighbors() → Leiden 聚类 ，支持 spatial graph，或 BayesSpace/SpaGCN。
- Identify spatial domains 、 tissue regions by combining expression 、 spatial information。
- sc.pp.neighbors(adata，n_neighbors=15，n_pcs=30) sc.tl.leiden(adata，resolution=0.5，key_added='leiden')。
原始文档
SKILL.md 摘录
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.
适用场景
- 适合在identifying tissue domains 或 spatial regions时使用。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- spatial-neighbors - Build spatial graphs (prerequisite)
- spatial-statistics - Compute spatial statistics per domain
- single-cell/clustering - Standard clustering methods
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