bio-workflows-imc-pipeline:Imaging mass cytometry:分割 → phenotyping → spatial analysis。
bio-imaging-mass-cytometry-spatial-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
bio-imaging-mass-cytometry-spatial-analysis:Spatial analysis of cell neighborhoods 、 interactions in IMC data。 Covers neighbor graphs,spatial statistics,、 interaction testing。 适合在analyzing spatial relationships between cell types,testing ,用于 neighborhood enrichment,或 identifying cell-cell interaction patterns in imaging mass cytometry data时使用。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-imaging-mass-cytometry-spatial-analysis
- 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(),squidpy.gr.nhood_enrichment()。
- 分析 spatial cell interactions in my IMC data" → Build spatial neighborhood graphs,test ,用于 cell-cell interaction enrichment,、 identify spatial domains ,面向 multiplexed imaging data. Python:squidpy.gr.spatial_neighbors(),squidpy.gr.nhood_enrichment()。
- adata = ad.read_h5ad('imc_phenotyped.h5ad')。
原始文档
SKILL.md 摘录
Build Spatial Graph
import squidpy as sq
import anndata as ad
## Build spatial neighbor graph
sq.gr.spatial_neighbors(adata, coord_type='generic', delaunay=True)
## Or by distance
sq.gr.spatial_neighbors(adata, coord_type='generic', radius=50) # 50 pixels
print(f'Built graph with {adata.obsp["spatial_connectivities"].nnz} edges')
适用场景
- 适合在analyzing spatial relationships between cell types,testing ,用于 neighborhood enrichment,或 identifying cell-cell interaction patterns in imaging mass cytometry data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
- phenotyping - Assign cell types first
- spatial-transcriptomics/spatial-statistics - Similar spatial methods
- single-cell/cell-communication - Interaction concepts
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