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tooluniverse-spatial-omics-analysis

Spatial multi-omics interpretation pipeline. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into biological insights via domain-by-domain characterization, cell-type composition, spatial gene expression patterns, RNA+protein+metabolite integration. Use for Visium, MERFISH, seqFISH, Slide-seq, spatial proteomics, and spatial multi-omics interpretation. Goes beyond statistics to disease mechanisms and therapeutic opportunities.

skills/tooluniverse-spatial-omics-analysis/SKILL.md

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Repository stars (not skill ratings): 1,779
Repository last updated: Nov 8, 2026
License: Apache-2.0

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test_spatial_omics.pyruns · 52,650 bytes
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# Spatial Multi-Omics Analysis Pipeline

Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights.

**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