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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.
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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:
Report-first approach - Create report file FIRST, then populate progressively
Domain-by-domain analysis - Characterize each spatial region independently before comparison
Gene-list-centric - Analyze user-provided SVGs and marker genes with ToolUniverse databases
Biological interpretation - Go beyond statistics to explain biological meaning of spatial patterns
Disease focus - Emphasize disease mechanisms and therapeutic opportunities when disease context is provided
Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
Multi-modal thinking - Integrate RNA, protein, and metabolite information when available
Validation guidance - Suggest experimental validation approaches for key findings
- Every statement must cite tool/database source
Source references
English-first queries - Always use English terms in tool calls
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
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 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
NOT for: Single gene interpretation (use target-research), variant interpretation, drug safety, bulk RNA-seq, GWAS analysis.
Input Parameters
Parameter
Required
Description
Example
svgs
Yes
Spatially variable genes
['EGFR', 'CDH1', 'VIM', 'MYC', 'CD3E']
tissue_type
Yes
Tissue/organ type
brain, liver, lung, breast
technology
No
Spatial omics platform
10x Visium, MERFISH, DBiTplus
disease_context
No
Disease if applicable
breast cancer, Alzheimer disease
spatial_domains
No
Domain -> marker genes dict
{'Tumor core': ['MYC','EGFR']}
cell_types
No
Cell types from deconvolution
['Epithelial', 'T cell']
proteins
No
Proteins detected (multi-modal)
['CD3', 'PD-L1', 'Ki67']
metabolites
No
Metabolites (SpatialMETA)
['glutamine', 'lactate']
Spatial Omics Integration Score (0-100)
Data Completeness (0-30): SVGs (5), Disease context (5), Spatial domains (5), Cell types (5), Multi-modal (5), Literature (5)
