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bio-spatial-transcriptomics-spatial-neighbors
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-neighbors
- 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(adata，coord_type='generic'，n_neighs=6)。
- 构建 spatial neighborhood graph" → Construct spatial connectivity graphs ，使用 k-nearest neighbors，Delaunay triangulation，或 radius-based methods ，用于 downstream spatial statistics. Python：squidpy.gr.spatial_neighbors(adata，coord_type='generic'，n_neighs=6)。
- 构建 spatial neighbor graphs ，用于 connectivity-based analyses。
- sq.gr.spatial_neighbors(adata，n_neighs=6，coord_type='generic')。
原始文档
SKILL.md 摘录
Build K-Nearest Neighbors Graph
Goal: Construct a spatial KNN graph connecting each spot to its nearest spatial neighbors.
Approach: Use Squidpy's spatial_neighbors with k-nearest neighbors on coordinate distances.
## Check the graph
print(f"Connectivities shape: {adata.obsp['spatial_connectivities'].shape}")
print(f"Distances shape: {adata.obsp['spatial_distances'].shape}")
Delaunay triangulation (natural neighbors)
sq.gr.spatial_neighbors(adata, delaunay=True, coord_type='generic')
适用场景
- 适合在building spatial neighborhood graphs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-statistics - Use neighbor graph for spatial statistics
- spatial-domains - Identify domains using spatial graph
- single-cell/clustering - Non-spatial neighbor graphs
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