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spatial-domains

Identify tissue regions from spatial transcriptomics data using clustering algorithms.

155|26|Updated Mar 14, 2026
Search Tags:#bioinformatics#spatial transcriptomics#clustering#domain identification#tissue region#niche discovery
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-domains
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spatial-domains
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-domains
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-domains

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, squidpy, matplotlib, numpy, pandas, scipy, scikit-learn, torch, SpaGCN, STAGATE_pyG, GraphST, banksy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the identification of distinct tissue regions and spatial niches within spatial transcriptomics data, eliminating the need for manual, inconsistent parameter tuning across different clustering methods.

Core Features & Use Cases

  • Automated Domain Identification: Utilizes multiple advanced algorithms (Leiden, SpaGCN, STAGATE, GraphST, BANKSY) to partition spatial transcriptomics data into biologically meaningful domains.
  • Visualization & Reporting: Generates annotated spatial maps, UMAP plots, and comprehensive reports detailing domain characteristics and reproducibility.
  • Use Case: A researcher has a Visium dataset and wants to identify distinct cell populations within different anatomical regions of a tumor. This Skill can automatically cluster the spatial spots into these regions, visualize them on the tissue, and provide a summary report.

Quick Start

Use the spatial-domains skill to identify tissue regions in the provided 'my_spatial_data.h5ad' file.

Frequently Asked Questions about spatial-domains

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

FAQPage Schema
How do I identify tissue regions and spatial niches from spatial transcriptomics data?▼

To identify tissue regions from spatial transcriptomics data, you can use automated algorithms like Leiden, SpaGCN, STAGATE, GraphST, or BANKSY to cluster spots into biologically meaningful domains and generate annotated spatial maps.

Can I use my AnnData format Visium dataset for spatial domain identification?▼

Yes, spatial domain identification supports input in AnnData format, allowing you to directly process Visium datasets to partition spatial spots into distinct anatomical regions and visualize them on the tissue.

What is the best way to cluster spatial transcriptomics spots into distinct anatomical regions?▼

The best way to cluster spatial transcriptomics spots is using graph-based or deep learning algorithms such as STAGATE, SpaGCN, or BANKSY, which partition data into spatial domains while generating comprehensive summary statistics.

Does SpaGCN or STAGATE work better for discovering spatial niches in tumor data?▼

Both SpaGCN and STAGATE are supported for discovering spatial niches in tumor data. The Skill automates parameter tuning across these clustering methods to partition spatial transcriptomics data into biologically meaningful domains.

What visualizations and reports are generated after spatial domain clustering?▼

Spatial domain clustering generates annotated spatial maps, UMAP plots, and comprehensive reports detailing domain characteristics and reproducibility bundles for multi-omics analysis.

Do I need preprocessed spatial transcriptomics data before running tissue region identification?▼

Yes, you need preprocessed spatial transcriptomics data in AnnData format before running tissue region identification, as the Skill automates partitioning spots into biologically meaningful domains from this input.

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