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Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data

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Listed:
  • Guanao Yan

    (University of California)

  • Shuo Harper Hua

    (Stanford University)

  • Jingyi Jessica Li

    (University of California
    University of California
    University of California
    University of California)

Abstract

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 34 state-of-the-art methods, classifying SVGs into three categories: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

Suggested Citation

  • Guanao Yan & Shuo Harper Hua & Jingyi Jessica Li, 2025. "Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56080-w
    DOI: 10.1038/s41467-025-56080-w
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