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
- Source references - Every statement must cite tool/database source
- 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)
Biological Insight (0-40): Pathway enrichment FDR<0.05 (10), Cell-cell interactions (10), Disease mechanism (10), Druggable targets (10)
Evidence Quality (0-30): Cross-database validation 3+ DBs (10), Clinical validation (10), Literature support (10)
| Score | Tier | Interpretation |
|---|---|---|
| 80-100 | Excellent | Comprehensive characterization, strong insights, druggable targets |
| 60-79 | Good | Good pathway/interaction analysis, some therapeutic context |
| 40-59 | Moderate | Basic enrichment, limited domain comparison |
| 0-39 | Limited | Minimal data, gene-level annotation only |
Evidence Grading
| Tier | Criteria | Examples |
|---|---|---|
| [T1] | Direct human/clinical evidence | FDA-approved drug, validated biomarker |
| [T2] | Experimental evidence | Validated spatial pattern, known L-R pair |
| [T3] | Computational/database evidence | PPI prediction, pathway enrichment |
| [T4] | Annotation/prediction only | GO annotation, text-mined association |
Analysis Phases Overview
Phase 0: Input Processing & Disambiguation (ALWAYS FIRST)
Resolve tissue/disease identifiers, establish analysis context. Get MONDO/EFO IDs for disease queries.
- Tools:
OpenTargets_get_disease_id_description_by_name,OpenTargets_get_disease_description_by_efoId,HPA_search_genes_by_query
Phase 1: Gene Characterization
Resolve gene IDs, annotate functions, tissue specificity, subcellular localization.
- Tools:
MyGene_query_genes,UniProt_get_function_by_accession,HPA_get_subcellular_location,HPA_get_rna_expression_by_source,HPA_get_comprehensive_gene_details_by_ensembl_id,HPA_get_cancer_prognostics_by_gene,UniProtIDMap_gene_to_uniprot
Phase 2: Pathway & Functional Enrichment
Identify enriched pathways globally and per-domain. Filter FDR < 0.05.
- Tools:
STRING_functional_enrichment(PRIMARY),ReactomeAnalysis_pathway_enrichment,GO_get_annotations_for_gene,kegg_search_pathway,WikiPathways_search
Phase 3: Spatial Domain Characterization
Characterize each domain biologically, assign cell types from markers, compare domains.
- Tools: Phase 2 tools +
HPA_get_biological_processes_by_gene,HPA_get_protein_interactions_by_gene
Phase 4: Cell-Cell Interaction Inference
Predict communication from spatial patterns. Check ligand-receptor pairs across domains.
- Tools:
STRING_get_interaction_partners,STRING_get_protein_interactions,intact_search_interactions,Reactome_get_interactor,DGIdb_get_drug_gene_interactions
Phase 5: Disease & Therapeutic Context
Connect to disease mechanisms, identify druggable targets, find clinical trials.
- Tools:
OpenTargets_get_associated_targets_by_disease_efoId,OpenTargets_get_target_tractability_by_ensemblID,OpenTargets_get_associated_drugs_by_target_ensemblID,search_clinical_trials,DGIdb_get_gene_druggability,civic_search_genes
Phase 6: Multi-Modal Integration
Integrate protein/RNA/metabolite data. Compare spatial RNA with protein detection.
- Tools:
HPA_get_subcellular_location,HPA_get_rna_expression_in_specific_tissues,Reactome_map_uniprot_to_pathways,kegg_get_pathway_info
Phase 7: Immune Microenvironment (Cancer/Inflammation only)
Classify immune cells, check checkpoint expression, assess Hot vs Cold vs Excluded patterns.
- Tools:
STRING_functional_enrichment,OpenTargets_get_target_tractability_by_ensemblID,iedb_search_epitopes
Phase 8: Literature & Validation Context
Search published evidence, suggest validation experiments (smFISH, IHC, PLA).
- Tools:
PubMed_search_articles,openalex_literature_search
Data Discovery: HuBMAP Spatial Atlas Tools
Use HuBMAP tools to find published spatial biology reference datasets for comparison, validation, or cross-study analysis.
| Tool | Purpose | Key Parameters |
|---|---|---|
HuBMAP_search_datasets | Search published spatial datasets by organ/assay/keyword | organ (code: "LK"=Kidney, "BR"=Brain, "LU"=Lung, etc.), dataset_type ("RNAseq", "CODEX", "MALDI"), query, limit |
HuBMAP_list_organs | List all available organs with codes and UBERON IDs | (no required params) |
HuBMAP_get_dataset | Get detailed metadata for a specific HuBMAP dataset | hubmap_id (e.g. "HBM626.FHJD.938") |
When to use: Phase 0 (find reference datasets for the tissue), Phase 8 (cross-reference findings with published HuBMAP atlas data).
See phase-procedures.md for detailed workflows, decision logic, and tool parameter specifications per phase.
Report Structure
Create file: {tissue}_{disease}_spatial_omics_report.md
# Spatial Multi-Omics Analysis Report: {Tissue Type}
**Report Generated**: {date} | **Technology**: {platform}
**Tissue**: {tissue_type} | **Disease**: {disease or "Normal tissue"}
**Total SVGs**: {count} | **Spatial Domains**: {count}
**Spatial Omics Integration Score**: (calculated after analysis)
## Executive Summary
## 1. Tissue & Disease Context
## 2. Spatially Variable Gene Characterization
- 2.1 Gene ID Resolution
- 2.2 Tissue Expression Patterns
- 2.3 Subcellular Localization
- 2.4 Disease Associations
## 3. Pathway Enrichment Analysis
- 3.1 STRING, 3.2 Reactome, 3.3-3.5 GO (BP, MF, CC)
## 4. Spatial Domain Characterization (per-domain + comparison)
## 5. Cell-Cell Interaction Inference
- 5.1 PPI, 5.2 Ligand-Receptor, 5.3 Signaling Pathways
## 6. Disease & Therapeutic Context
- 6.1 Disease Gene Overlap, 6.2 Druggable Targets, 6.3 Drug Mechanisms, 6.4 Trials
## 7. Multi-Modal Integration (if data available)
## 8. Immune Microenvironment (if relevant)
## 9. Literature & Validation Context
## Spatial Omics Integration Score (breakdown table)
## Completeness Checklist
## References (tools used, database versions)See report-template.md for full template with table structures.
Completeness Checklist
- Gene ID resolution complete
- Tissue expression patterns analyzed (HPA)
- Subcellular localization checked (HPA)
- Pathway enrichment complete (STRING + Reactome)
- GO enrichment complete (BP + MF + CC)
- Spatial domains characterized individually
- Domain comparison performed
- PPI analyzed (STRING)
- Ligand-receptor pairs identified
- Disease associations checked (OpenTargets)
- Druggable targets identified
- Multi-modal integration performed (if data available)
- Immune microenvironment characterized (if relevant)
- Literature search completed
- Validation recommendations provided
- Integration Score calculated
- Executive summary written
- All sections have source citations
Common Use Cases
- Cancer Spatial Heterogeneity: Visium with tumor/stroma/immune domains -> pathways, immune infiltration, druggable targets, checkpoints
- Brain Tissue Zonation: MERFISH with neuronal subtypes -> synaptic signaling, receptors, hippocampal zonation
- Liver Metabolic Zonation: Periportal vs pericentral -> CYP450, Wnt gradient, drug metabolism enzymes
- Tumor-Immune Interface: DBiTplus RNA+protein -> checkpoint L-R pairs, immune exclusion, multi-modal concordance
- Developmental Patterns: Morphogen gradients (Wnt, BMP, FGF, SHH), TF patterns, cell fate genes
- Disease Progression: Disease gradient -> inflammatory response, neuronal loss, therapeutic windows
Reference Files
- phase-procedures.md - Detailed phase workflows, decision logic, tool usage per phase
- tool-reference.md - Tool parameter names, response formats, fallback strategies, limitations
- reference-data.md - Cell type markers, ligand-receptor pairs, immune checkpoint reference
- report-template.md - Full report template with all table structures
- test_spatial_omics.py - Test suite
Summary
Spatial Multi-Omics Analysis provides:
- Gene characterization (ID resolution, function, localization, tissue expression)
- Pathway & functional enrichment (STRING, Reactome, GO, KEGG)
- Spatial domain characterization (per-domain and cross-domain)
- Cell-cell interaction inference (PPI, ligand-receptor, signaling)
- Disease & therapeutic context (disease genes, druggable targets, trials)
- Multi-modal integration (RNA-protein concordance, metabolic pathways)
- Immune microenvironment (cell types, checkpoints, immunotherapy)
- Literature context & validation recommendations
Outputs: Markdown report with Spatial Omics Integration Score (0-100) Uses: 70+ ToolUniverse tools across 9 analysis phases Time: ~10-20 minutes depending on gene list size