omics-spatial

Load spatial transcriptomics data and perform quality control with scverse tools.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/Minions-Land/AutOmicScience --skill omics-spatial
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omics-spatial
Source: https://github.com/Minions-Land/AutOmicScience/tree/main/skills/omics/spatial
Command: npx skills add https://github.com/Minions-Land/AutOmicScience --skill omics-spatial

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires squidpy, scanpy, spatialdata, spatialdata-io, anndata, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexity of spatial transcriptomics by providing a standardized, reproducible framework for loading, quality-controlling, and analyzing spatial data across multiple platforms like Visium, Xenium, and MERFISH.

Core Features & Use Cases

  • Multi-Platform Support: Unified loading and validation for Visium, Xenium, MERFISH, CosMx, and Stereo-seq.
  • Spatial Statistics: Advanced analysis including neighborhood enrichment, co-occurrence, and spatially variable gene detection using squidpy.
  • Cell-Type Mapping: Integration of scRNA-seq references for spot deconvolution and label transfer via cell2location and Tangram.
  • Use Case: Analyze a Visium slide to identify spatially contiguous tissue domains and map cell-type composition to understand the tumor microenvironment.

Quick Start

Use the omics-spatial skill to load your spatial data and perform initial quality control by running the omics_runtime read_spatial command followed by the spatial_qc method.

Frequently Asked Questions about omics-spatial

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

FAQPage Schema
How do I perform spatial transcriptomics quality control across different platforms like Visium and Xenium?▼

Spatial transcriptomics quality control across Visium and Xenium is performed by loading data into spatialdata containers and applying standardized spatial_qc methods. This ensures reproducible, evidence-backed biological insights across multi-modal platforms.

What is the best way to identify spatially contiguous tissue domains in a Visium slide?▼

Identifying spatially contiguous tissue domains in a Visium slide requires running domain detection algorithms integrated with squidpy. This process maps cell-type composition to help understand the tumor microenvironment.

Can I use scRNA-seq references for cell-type mapping and spot deconvolution in spatial data?▼

Yes, scRNA-seq references can be integrated for spot deconvolution and label transfer in spatial data using cell2location and Tangram. This integrates with scverse tools to map cell-type composition accurately.

Does this spatial transcriptomics analysis support MERFISH and Stereo-seq platforms?▼

Spatial transcriptomics analysis supports MERFISH, Stereo-seq, Visium, Xenium, and CosMx platforms through unified loading and validation. Multi-platform support ensures standardized data handling across diverse bioinformatics research.

How do I detect spatially variable genes and perform neighborhood enrichment analysis?▼

Detecting spatially variable genes and performing neighborhood enrichment analysis utilizes advanced spatial statistics via squidpy. This includes co-occurrence analysis to provide evidence-backed biological insights within spatialdata containers.