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spatial-genes

Detect spatially variable genes in AnnData objects using Moran's I, SpatialDE, SPARK-X, and FlashS.

155|26|Updated Mar 14, 2026
Search Tags:#spatial#genes#spatialde#morans i#sparkx#flashs
One-click install
npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-genes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: spatial-genes
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/spatial/spatial-genes
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill spatial-genes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, squidpy, matplotlib, numpy, pandas, SpatialDE, rpy2, flashs, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the identification of genes whose expression is not randomly distributed across tissue samples, which is crucial for understanding tissue architecture and cellular microenvironments.

Core Features & Use Cases

  • Multiple Detection Methods: Supports Moran's I, SpatialDE, SPARK-X, and FlashS for robust gene discovery.
  • Visualization: Generates plots of top spatially variable genes on tissue coordinates and UMAPs.
  • Use Case: In a spatial transcriptomics experiment, use this Skill to find genes that mark specific tissue regions or cell types, aiding in the interpretation of spatial organization.

Quick Start

Use the spatial-genes skill to find spatially variable genes in my data using Moran's I.

Frequently Asked Questions about spatial-genes

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

FAQPage Schema
How do I run SpatialDE or SPARK-X on my AnnData object for SVG detection?▼

Spatially variable genes are genes whose expression levels show non-random distributions across tissue coordinates. They are detected using statistical approaches like Moran's I or SPARK-X to measure spatial autocorrelation in spatial transcriptomics datasets.

Do I need spatial coordinates in my AnnData object to use these spatial gene detection methods?▼

Yes, you can visualize spatially variable genes directly on tissue coordinates and UMAPs. The Skill generates plots of top ranked genes, aiding in the interpretation of spatial organization and cellular microenvironments.

What is the best way to compare Moran's I, SpatialDE, and FlashS for finding spatially variable genes?▼

Yes, spatial coordinates are required. The Skill analyzes spatial transcriptomics data by evaluating expression distributions across tissue coordinates, so your AnnData object must include this spatial information to perform accurate spatially variable gene detection.

Why use multiple statistical methods like SpatialDE and SPARK-X for spatial transcriptomics analysis?▼

Comparing methods provides robust gene discovery. Moran's I measures spatial autocorrelation, SpatialDE uses Gaussian process regression, SPARK-X relies on a scalable Poisson model, and FlashS offers fast detection, allowing you to validate top spatially variable genes across multiple statistical approaches.

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