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bio-spatial-transcriptomics-spatial-statistics
Maintainer FreedomIntelligence · Last updated April 1, 2026
Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-statistics
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Compute spatial statistics and identify spatially variable features.
- sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=6).
Source Doc
Excerpt From SKILL.md
Compute Spatial Autocorrelation (Moran's I)
Goal: Identify genes whose expression is spatially autocorrelated across tissue.
Approach: Build a spatial neighbor graph, then compute Moran's I statistic per gene to measure clustering of similar values.
"Find spatially variable genes" -> Compute Moran's I autocorrelation on the spatial neighbor graph to rank genes by spatial patterning.
## Compute Moran's I for all genes (can be slow)
sq.gr.spatial_autocorr(adata, mode='moran')
## Or for specific genes
sq.gr.spatial_autocorr(adata, mode='moran', genes=['GENE1', 'GENE2', 'GENE3'])
Use cases
- Use when computing spatial autocorrelation or co-occurrence statistics.
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-domains - Identify spatial domains
- spatial-visualization - Visualize spatial statistics
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