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tooluniverse-spatial-omics-analysis

Interprets spatially variable genes and spatial domains into pathway, interaction, and therapeutic insights.

1.7k|254|Updated Mar 3, 2025
Search Tags:#bioinformatics#pathway enrichment#cell-cell interaction#spatial transcriptomics#multi-omics#visium#druggable targets
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-spatial-omics-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-spatial-omics-analysis
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-spatial-omics-analysis
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-spatial-omics-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Spatial omics experiments (Visium, MERFISH, seqFISH, Slide-seq) produce lists of spatially variable genes and domain annotations, but turning those gene lists into biological meaning requires querying dozens of databases and synthesizing pathway, interaction, disease, and drug evidence manually. This Skill automates that interpretation pipeline using 70+ ToolUniverse database tools.

Core Features & Use Cases

  • Domain-by-domain characterization: Resolves gene IDs, tissue expression, subcellular localization, and runs STRING/Reactome/GO enrichment per spatial domain with FDR filtering.
  • Cell-cell interaction and therapeutic context: Infers ligand-receptor pairs and PPI networks, then connects findings to disease genes, druggable targets, approved drugs, and clinical trials via OpenTargets, DGIdb, and CIViC.
  • Multi-modal and immune analysis: Integrates RNA, protein, and metabolite data, classifies immune infiltration (Hot/Cold/Excluded), and grades all evidence T1-T4 with a 0-100 Spatial Omics Integration Score.
  • Use Case: Given SVGs from a breast cancer Visium experiment with tumor/stroma/immune domains, produce a structured Markdown report covering enriched pathways, checkpoint ligand-receptor pairs, druggable targets in the tumor core, and validation experiment recommendations.

Quick Start

Analyze these spatially variable genes from my 10x Visium breast cancer sample with tumor core, stroma, and immune domains, and generate a full spatial multi-omics interpretation report.

Frequently Asked Questions about tooluniverse-spatial-omics-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I interpret spatially variable genes from Visium data?▼

Provide your SVG list, tissue type, and optional disease context and domain markers. The pipeline resolves gene IDs, runs STRING and Reactome enrichment with FDR < 0.05 filtering, characterizes each spatial domain, and produces a structured Markdown report with an integration score.

What spatial omics platforms does this analysis support?▼

It supports 10x Visium, MERFISH, seqFISH, Slide-seq, DBiTplus, and spatial proteomics or metabolomics data. The analysis works on gene lists and domain annotations rather than raw spatial matrices, so any platform producing SVGs is compatible.

Can it infer cell-cell interactions from spatial transcriptomics?▼

Yes, it predicts interactions using STRING protein-protein interaction networks filtered at confidence score above 0.7 and checks known ligand-receptor pairs across spatial domains. Note this is based on co-expression and known pairs, not spatial proximity statistics like CellChat or NicheNet.

Does it identify druggable targets in specific tissue regions?▼

Yes, it queries OpenTargets tractability, DGIdb druggability categories, approved drugs, and clinical trials for genes in each spatial domain. Results are graded by evidence tier from T1 clinical evidence to T4 computational annotation.

What are the limitations of this spatial analysis approach?▼

It analyzes gene lists, not raw spatial matrices, so it cannot perform spatial statistics like Moran's I, image analysis, or deconvolution. Use external tools like BayesSpace, cell2location, or RCTD for deconvolution before running this interpretation pipeline.

How is evidence quality graded in the analysis report?▼

Every finding is graded T1 through T4: T1 is direct human or clinical evidence such as FDA-approved drugs, T2 is experimental evidence, T3 is computational or database evidence, and T4 is annotation-only. A 0-100 Spatial Omics Integration Score summarizes completeness, insight, and evidence quality.

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