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bio-spatial-transcriptomics-spatial-neighbors
Maintainer FreedomIntelligence · Last updated April 1, 2026
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.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-neighbors
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Python: squidpy.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6).
- Build a spatial neighborhood graph" → Construct spatial connectivity graphs using k-nearest neighbors, Delaunay triangulation, or radius-based methods for downstream spatial statistics. Python: squidpy.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6).
- Build spatial neighbor graphs for connectivity-based analyses.
- sq.gr.spatial_neighbors(adata, n_neighs=6, coord_type='generic').
Source Doc
Excerpt From SKILL.md
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')
Use cases
- Use when building spatial neighborhood graphs.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- 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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