tooluniverse-spatial-omics-analysis
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characte
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-spatial-omics-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Transforms user-provided spatially variable genes, tissue type, and optional disease context into a comprehensive interpretation across spatial domains, including pathway enrichment, cell–cell interactions, druggable targets, immune microenvironment, and multi-modal integration, then produces a detailed markdown report with a Spatial Omics Integration Score.
How it works
- Follows a report-first approach: creates the report file at the start, then fills sections progressively.
- Performs domain-by-domain analysis: characterizes each spatial region independently before comparisons.
- Focuses on gene lists provided by SVGs and markers, leveraging ToolUniverse databases for interpretation.
- Integrates disease focus to emphasize mechanisms and therapeutic opportunities when disease context is supplied.
- Grades evidence using a defined tier system (T1–T4) and compiles validation guidance.
- Requires input parameters such as svgs, tissue_type, and optional disease_context, spatial_domains, cell_types, proteins, and metabolites; uses English-first terms in tool calls.
- Produces sections across pathway enrichment (STRING/Reactome/GO), domain characterization, cell–cell interactions, therapeutic context, multi-modal integration, immune microenvironment (if relevant), literature validation context, and a completeness checklist.
When to use it
- When spatially variable genes from spatial transcriptomics experiments are provided.
- To interpret spatial domain or cluster biology, tumor microenvironment heterogeneity, or tissue zonation.
- To perform pathway enrichment, understand cell–cell interactions, or identify druggable targets in spatial regions.
- To integrate spatial transcriptomics with proteomics or other modalities if data are available.
What it can touch
- ToolUniverse tools accessed via the included executable scripts and the claude-code interface. (Tools and scripts are invoked to perform analysis steps and generate the report.)
Caveats
- License is NOASSERTION; behavior and availability depend on repository/toolchain terms.
- The skill requires properly provided inputs (svgs, tissue_type) and may rely on external databases for enrichment and targeting information.
- The scaffold emphasizes evidence grading and source attribution; actual results depend on data quality and database coverage.
# 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 covering pathway enrichment, cell-cell interactions, druggable targets, immune microenvironment, and multi-modal integration. **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
- When to Use This Skill
- Input Parameters
- Spatial Omics Integration Score (0-100)
- Score Components
- Score Interpretation
- Evidence Grading System
- Report Template
- Phase 0: Input Processing & Disambiguation (ALWAYS FIRST)
- Tools Used
- Workflow
- Decision Logic
- Phase 1: Gene Characterization
- Batch Strategy for Large Gene Lists
- Phase 2: Pathway & Functional Enrichment
What does the tooluniverse-spatial-omics-analysis skill do?
Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characte
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-spatial-omics-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.