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spatial-transcriptomics

Preprocess spatial transcriptomics data to identify domains and deconvolute cell types.

25|5|Updated Mar 22, 2026

Search Tags:#visualization #deconvolution #domain-detection #spatial-transcriptomics #spatial-omics #neighborhood-analysis

One-click install

npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill spatial-transcriptomics

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: spatial-transcriptomics
Source: https://github.com/zongtingwei/Bioclaw_Skills_Hub/tree/main/skills/single-cell-and-spatial/spatial-transcriptomics
Command: npx skills add https://github.com/zongtingwei/Bioclaw_Skills_Hub --skill spatial-transcriptomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Spatial transcriptomics workflows are often scattered across heterogeneous tools and notes; this Skill provides a structured, repeatable pipeline for preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready maps.

Core Features & Use Cases

Spatial preprocessing and normalization that preserve spatial coordinates
Domain detection and deconvolution to infer cell-type composition in tissue spots
Neighborhood analysis and generation of publication-ready spatial maps
Use case: analyze a tissue section to identify spatial domains and associated cell-type enrichment

Quick Start

Begin by validating spatial inputs and then run the domain detection workflow on your spatial expression data using the preferred scanpy-like tools.

Frequently Asked Questions about spatial-transcriptomics

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

FAQPage Schema

How do I map spatial gene expression to tissue structure?▼

Neighborhood analysis identifies spatial relationships between cell types across tissue spots. It generates neighborhood-aware maps that show cell-type enrichment and spatial domain organization within the tissue section.

How do I deconvolute cell-type signals in spatial transcriptomics data?▼

Neighborhood analysis identifies spatial relationships between cell types across tissue spots. It generates neighborhood-aware maps that visualize cell-type enrichment and tissue spatial domains.

What is the best way to identify spatial domains in tissue sections?▼

Neighborhood analysis identifies spatial relationships between cell types across tissue spots. It generates neighborhood-aware maps that visualize cell-type enrichment and tissue spatial domains.

Can I use this spatial transcriptomics pipeline with single-cell references?▼

Neighborhood analysis identifies spatial relationships between cell types across tissue spots. It generates neighborhood-aware maps that visualize cell-type enrichment and tissue spatial domains.

How do I generate publication-ready spatial maps from spot-based coordinate data?▼

Neighborhood analysis identifies spatial relationships between cell types across tissue spots. It generates neighborhood-aware maps that visualize cell-type enrichment and tissue spatial domains.

Do I need scanpy-like tooling to preprocess spatial transcriptomics data?▼

Neighborhood analysis identifies spatial relationships between cell types across tissue spots. It generates neighborhood-aware maps that visualize cell-type enrichment and tissue spatial domains.

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