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tooluniverse-spatial-omics-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identificatio….
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-spatial-omics-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights covering pathway enrichment, cell-cell interactions, druggable targets, immune microenvironment, and 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 and marker genes with ToolUniverse databases 4. Biological interpretation - Go beyond statistics to explain biological meaning of spatial patterns 5. Disease focus - Emphasize disease mechanisms and 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, and metabolite information when available 8. Validation guidance - Suggest experimental validation approaches for 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: {platform} Tissue: {tissue_type} Disease Context: {disease or "Normal tissue"} Total SVGs Analyzed: {count} Spatial Domains: {count} Spatial Omics Integration Score: (to be calculated).
Source Doc
Excerpt From SKILL.md
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'] |
Use cases
- 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.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
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