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tooluniverse-spatial-omics-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
tooluniverse-spatial-omics-analysis：Computational analysis 框架 ，用于 spatial multi-omics data integration。 Given spatially variable genes (SVGs)，spatial domain annotations，tissue type，、 disease context ，面向 spatial transcriptomics/proteomics experiments (10x Visium，MERFISH，DBiTplus，SLIDE-seq，etc.)，performs comprehensive biological interpretation ，涵盖 pathway enrichment，cell-cell interaction inference，druggable target identificatio…。
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-spatial-omics-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
- Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs)，domain annotations，、 tissue context into actionable biological insights covering pathway enrichment，cell-cell interactions，druggable targets，immune microenvironment，、 multi-modal integration。
- KEY PRINCIPLES：1. Report-first approach - Create report file FIRST，then populate progressively 2. Domain-by-domain analysis - Characterize each spatial region independently before comparison 3. Gene-list-centric - Analyze user-provided SVGs 、 marker genes ，支持 ToolUniverse databases 4. Biological interpretation - Go beyond statistics to explain biological meaning of spatial patterns 5. Disease focus - Emphasize disease mechanisms 、 therapeutic opportunities when disease context is provided 6. Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational) 7. Multi-modal thinking - Integrate RNA，protein，、 metabolite information when available 8. Validation guidance - Suggest experimental validation approaches ，用于 key findings 9. Source references - Every statement must cite tool/database source 10. Completeness checklist - Mandatory section showing analysis coverage 11. English-first queries - Always use English terms in tool calls. Respond in user's language。
- Report Generated：{date} Technology：{平台} Tissue：{tissue_type} Disease Context：{disease 或 "Normal tissue"} Total SVGs Analyzed：{count} Spatial Domains：{count} Spatial Omics Integration Score：(to be calculated)。
原始文档
SKILL.md 摘录
When to Use This Skill
Apply when users:
- Provide spatially variable genes from spatial transcriptomics experiments
- Ask about biological interpretation of spatial domains/clusters
- Need pathway enrichment analysis of spatial gene expression data
- Want to understand cell-cell interactions from spatial data
- Ask about tumor microenvironment heterogeneity from spatial omics
- Need druggable targets in specific spatial regions
- Ask about tissue zonation patterns (liver, brain, kidney)
- Want to integrate spatial transcriptomics + proteomics data
- Ask about immune infiltration patterns from spatial data
- Need to compare healthy vs disease regions spatially
- Ask "What pathways are enriched in this tumor core vs tumor margin?"
- Ask "What cell-cell interactions occur in this spatial domain?"
NOT for (use other skills instead):
- Single gene interpretation without spatial context -> Use
tooluniverse-target-research - Variant interpretation -> Use
tooluniverse-variant-interpretation - Drug safety profiling -> Use
tooluniverse-adverse-event-detection - Disease-only analysis without spatial data -> Use
tooluniverse-multiomic-disease-characterization - GWAS analysis -> Use
tooluniverse-gwas-*skills - Bulk RNA-seq (non-spatial) -> Use
tooluniverse-systems-biology
Input Parameters
| Parameter | Required | Description | Example |
|---|---|---|---|
| svgs | Yes | Spatially variable genes (gene symbols) | ['EGFR', 'CDH1', 'VIM', 'MYC', 'CD3E'] |
| tissue_type | Yes | Tissue/organ type | brain, liver, lung, breast, skin |
| technology | No | Spatial omics platform used | 10x Visium, MERFISH, DBiTplus, SLIDE-seq |
| disease_context | No | Disease if applicable | breast cancer, Alzheimer disease, liver cirrhosis |
| spatial_domains | No | Dict mapping domain name to marker genes | {'Tumor core': ['MYC','EGFR'], 'Stroma': ['VIM','COL1A1']} |
| cell_types | No | Cell types identified in deconvolution | ['Epithelial', 'T cell', 'Macrophage', 'Fibroblast'] |
| proteins | No | Proteins detected (if multi-modal) | ['CD3', 'CD8', 'PD-L1', 'Ki67'] |
| metabolites | No | Metabolites detected (if SpatialMETA) | ['glutamine', 'lactate', 'ATP'] |
适用场景
- 提供 spatially variable genes ，面向 spatial transcriptomics experiments。
- Ask about biological interpretation of spatial domains/clusters。
- Need pathway enrichment analysis of spatial gene expression data。
- Want to understand cell-cell interactions ，面向 spatial data。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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