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Interprets spatial multi-omics data (Visium, MERFISH, seqFISH, Slide-seq) into biological insights: domain characterization, cell-type composition, spatial gene expression patterns, and disease mechanisms using database-backed enrichment.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tooluniverse:tooluniverse-spatial-omics-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights.
Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights.
KEY PRINCIPLES:
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.
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.
Apply when users:
NOT for: Single gene interpretation (use target-research), variant interpretation, drug safety, bulk RNA-seq, GWAS analysis.
| 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'] |
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 |
| 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 |
Resolve tissue/disease identifiers, establish analysis context. Get MONDO/EFO IDs for disease queries.
OpenTargets_get_disease_id_description_by_name, OpenTargets_get_disease_description_by_efoId, HPA_search_genes_by_queryResolve gene IDs, annotate functions, tissue specificity, subcellular localization.
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_uniprotIdentify enriched pathways globally and per-domain. Filter FDR < 0.05.
STRING_functional_enrichment (PRIMARY), ReactomeAnalysis_pathway_enrichment, GO_get_annotations_for_gene, kegg_search_pathway, WikiPathways_searchCharacterize each domain biologically, assign cell types from markers, compare domains.
HPA_get_biological_processes_by_gene, HPA_get_protein_interactions_by_genePredict communication from spatial patterns. Check ligand-receptor pairs across domains.
STRING_get_interaction_partners, STRING_get_protein_interactions, intact_search_interactions, Reactome_get_interactor, DGIdb_get_drug_gene_interactionsConnect to disease mechanisms, identify druggable targets, find clinical trials.
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_genesIntegrate protein/RNA/metabolite data. Compare spatial RNA with protein detection.
HPA_get_subcellular_location, HPA_get_rna_expression_in_specific_tissues, Reactome_map_uniprot_to_pathways, kegg_get_pathway_infoClassify immune cells, check checkpoint expression, assess Hot vs Cold vs Excluded patterns.
STRING_functional_enrichment, OpenTargets_get_target_tractability_by_ensemblID, iedb_search_epitopesSearch published evidence, suggest validation experiments (smFISH, IHC, PLA).
PubMed_search_articles, openalex_literature_searchUse 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.
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.
Spatial Multi-Omics Analysis provides:
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
npx claudepluginhub mims-harvard/tooluniverse --plugin tooluniverseAnalyzes spatial transcriptomics data to map gene expression to tissue architecture, identifies spatially variable genes, and infers cell-cell interactions.
Integrates CellxGene Census single-cell RNA-seq data with ENCODE bulk experiments to resolve cell-type-specific expression for regulatory signal deconvolution and validation.
Queries the CELLxGENE Census of 61M+ single-cell RNA-seq observations by cell type, tissue, disease, or organism. Returns AnnData or PyTorch dataloaders for ML workflows.