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bio-spatial-transcriptomics-spatial-statistics
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bio-spatial-transcriptomics-spatial-statistics：Compute spatial statistics ，用于 spatial transcriptomics data ，使用 Squidpy。 Calculate Moran's I，Geary's C，spatial autocorrelation，co-occurrence analysis，、 neighborhood enrichment。 适合在computing spatial autocorrelation 或 co-occurrence statistics时使用。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-spatial-transcriptomics-spatial-statistics
- 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
- Compute spatial statistics 、 identify spatially variable features。
- sq.gr.spatial_neighbors(adata，coord_type='generic'，n_neighs=6)。
原始文档
SKILL.md 摘录
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'])
适用场景
- 适合在computing spatial autocorrelation 或 co-occurrence statistics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-domains - Identify spatial domains
- spatial-visualization - Visualize spatial statistics
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