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,

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 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 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 1. Cancer Spatial Heterogeneity : Visium with tumor/stroma/immune domains pathways, immune infiltration, druggable targets, checkpoints 2. Brain Tissue Zonation : MERFISH with neuronal subtypes synaptic signaling, receptors, hippocampal zonation 3. Liver Metabolic Zonation : Periportal vs pericentral CYP450, Wnt gradient, drug metabolism enzymes 4. Tumor Immune Interface : DBiTplus RNA+protein checkpoint L R pairs, immune exclusion, multi modal concordance 5. Developmental Patterns : Morphogen gradients (Wnt, BMP, FGF, SHH), TF patterns, cell fate genes 6. 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: 1. Gene characterization (ID resolution, function, localization, tissue expression) 2. Pathway & functional enrichment (STRING, Reactome, GO, KEGG) 3. Spatial domain characterization (per domain and cross domain) 4. Cell cell interaction inference (PPI, ligand receptor, signaling) 5. Disease & therapeutic context (disease genes, druggable targets, trials) 6. Multi modal integration (RNA protein concordance, metabolic pathways) 7. Immune microenvironment (cell types, checkpoints, immunotherapy) 8. 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

tooluniverse-spatial-omics-analysis