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spatial-domain-graphst

Detect spatial domains in spot-level transcriptomics data using GraphST clustering.

3|Updated Apr 3, 2026
Search Tags:#bioinformatics#domain-detection#spatial-transcriptomics#graph-neural-networks#adata#spatial-domain#graphst
One-click install
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill spatial-domain-graphst
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spatial-domain-graphst
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/spatial-domain-GraphST
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill spatial-domain-graphst

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identifies coherent spatial regions in spot-level spatial transcriptomics data by applying GraphST to learn domain-aware embeddings and assign domain labels per spot.

Core Features & Use Cases

  • GraphST-based detection of spatial domains on single-slice data with optional domain refinement.
  • Outputs: adata.obs['spatial_domain'] and adata.uns['spatial_domain_params'] to capture domain assignments and method details.
  • Use case: reveal tissue architecture by clustering spots into spatial domains and visualize domain maps.

Quick Start

Load your spot-level AnnData, run GraphST to compute spatial domains, and inspect adata.obs['spatial_domain'].

Frequently Asked Questions about spatial-domain-graphst

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

FAQPage Schema
How do I detect spatial domains in spot-level transcriptomics data?▼

You must normalize your AnnData object, identify highly variable genes, and compute embeddings before running GraphST. The Skill requires compatible preprocessing steps on single-slice gene expression data with spatial coordinates to accurately detect spatial domains.

What outputs do I get after running spatial domain detection with GraphST?▼

After running GraphST, you get adata.obs['spatial_domain'] containing the domain label assignments for each spot, and adata.uns['spatial_domain_params'] capturing method details for traceability. These outputs allow you to visualize domain maps and reveal tissue architecture.

Do I need to install GraphST separately to use this spatial domain detection?▼

Yes, GraphST installation is required to use this spatial domain detection Skill. You need a compatible Python environment with GraphST installed to process your spot-level AnnData and generate spatial domain embeddings and labels.

Can I use this Skill to detect spatial domains across multiple tissue slices?▼

No, this Skill applies to single-slice gene expression data with spatial coordinates. It processes one tissue slice at a time using GraphST clustering to partition spots into coherent spatial regions rather than handling multiple slices simultaneously.

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