Spatially resolved transcriptomics (SRT) advances the field of genomics by correlating gene expression measurements with spatial locations in tissues, unlike traditional single-cell RNA sequencing. The growing complexity of SRT data requires sophisticated statistical methods to derive insights. This article reviews 34 methods for detecting spatially variable genes (SVGs), emphasizing the importance of consistent definitions and methodologies for accurate interpretation. The authors introduce a classification framework for SVGs, shedding light on their biophysical significance and the statistical principles employed in detecting them, ultimately enhancing the understanding of tissue architecture and cellular dynamics.
The landscape of Spatially Resolved Transcriptomics (SRT) is transforming genomics by enabling researchers to quantify gene expression while preserving spatial context within tissues.
Identifying spatially variable genes (SVGs) is a critical step in understanding tissue organization, functional gene modules, and the heterogeneity of cellular interactions.
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