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
10. English-first queries - Always use English terms in tool calls