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Updated: Jul 2, 2025

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Recent advances in spatially variable gene detection in spatial transcriptomics.

Sikta Das Adhikari1,2, Jiaxin Yang1, Jianrong Wang1

  • 1Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

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|February 19, 2024
PubMed
Summary

This review covers methods for identifying spatially variable genes (SVGs) in spatial transcriptomics data. It highlights the importance of SVGs for biological understanding and downstream analyses.

Keywords:
Single cell RNA sequencingSpatial transcriptomicsSpatially resolved transcriptomicsSpatially variable genes

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics technologies enable gene expression analysis within tissue context.
  • Identifying spatially variable genes (SVGs) is a critical first step in spatial transcriptomics data analysis.
  • SVGs reveal tissue architecture and cellular functions.

Purpose of the Study:

  • To provide a selective review of current methods for detecting spatially variable genes (SVGs).
  • To discuss practical implementations and insights into the SVG detection literature.
  • To aid researchers in understanding and applying SVG detection techniques.

Main Methods:

  • Literature review of spatial transcriptomics analysis methods.
  • Focus on techniques for identifying genes with specific spatial expression patterns.
  • Selective curation of prominent SVG detection approaches.

Main Results:

  • A growing number of methods for SVG detection have emerged.
  • Various innovative concepts and discussions surround SVG identification.
  • The review offers insights into practical implementations of these methods.

Conclusions:

  • SVG detection is fundamental for advancing biological insights from spatial transcriptomics.
  • The field is rapidly evolving with new methods and concepts.
  • This review serves as a guide to the current landscape of SVG detection techniques.