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tooluniverse-spatial-transcriptomics
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Analyze spatial transcriptomics data to map gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, Slide-seq, and imaging-based platforms. Performs spatial clustering, domain identification, cell-cell proximity analysis, spatial gene expression patterns, tissue architecture mapping, and integration with single-cell data. Use when analyzing spatial transcriptomics datasets, studying tissue org….
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-spatial-transcriptomics
- 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
- Comprehensive analysis of spatially-resolved transcriptomics data to understand gene expression patterns in tissue architecture context. Combines expression profiling ，支持 spatial coordinates to reveal tissue organization，cell-cell interactions，、 spatially variable genes。
- User has spatial transcriptomics data (Visium，MERFISH，seqFISH，etc.)。
- Questions about tissue architecture 或 spatial organization。
- Spatial gene expression pattern analysis。
- Cell-cell proximity 或 neighborhood analysis requests。
原始文档
SKILL.md 摘录
Core Capabilities
| Capability | Description |
|---|---|
| Data Import | 10x Visium, MERFISH, seqFISH, Slide-seq, STARmap, Xenium formats |
| Quality Control | Spot/cell QC, spatial alignment verification, tissue coverage |
| Normalization | Spatial-aware normalization accounting for tissue heterogeneity |
| Spatial Clustering | Identify spatial domains with similar expression profiles |
| Spatial Variable Genes | Find genes with non-random spatial patterns |
| Neighborhood Analysis | Cell-cell proximity, spatial neighborhoods, niche identification |
| Spatial Patterns | Gradients, boundaries, hotspots, expression waves |
| Integration | Merge with scRNA-seq for cell type mapping |
| Ligand-Receptor Spatial | Map cell communication in tissue context |
| Visualization | Spatial plots, heatmaps on tissue, 3D reconstruction |
Phase 1: Data Import & Quality Control
Objective: Load spatial data and assess quality.
Supported platforms:
10x Visium (most common):
- Spots: 55μm diameter, ~50 cells per spot
- Resolution: ~5,000-10,000 spots per capture area
- Data: Expression matrix + spatial coordinates + H&E image
MERFISH/seqFISH (imaging-based):
- Single-cell resolution
- Targeted gene panels (100-10,000 genes)
- Absolute coordinates per cell
Slide-seq/Slide-seqV2:
- 10μm bead resolution
- Genome-wide profiling
Xenium (10x single-cell spatial):
- Single-cell resolution
- Large gene panels (300+ genes)
- Subcellular resolution
Data loading (Visium):
Quality Control:
-
Spot-level QC:
-
Spatial alignment verification:
Phase 2: Preprocessing & Normalization
Objective: Normalize data accounting for spatial heterogeneity.
Normalization:
Highly variable genes:
Spatial smoothing (optional):
适用场景
- User has spatial transcriptomics data (Visium，MERFISH，seqFISH，etc.)。
- Questions about tissue architecture 或 spatial organi。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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