Scholarlink

<p>Spatially variable gene (SVG) detection is one of the most important tasks in spatial transcriptomics (ST) data analysis. To identify SVGs from population-level ST data, current practices often call SVGs separately from individual slices and then combine, or select highly variable genes from the concatenated expression matrix of all subjects as a surrogate. These approaches fail to simultaneously account for the common and subject-specific spatial patterns, leading to low accuracy and power. To overcome this issue, we develop PopSVG, a statistical method for population-level SVG detection. PopSVG hierarchically models the spatial expression from a population of biological replicates, balancing inter-subject homogeneity and heterogeneity. After parameter estimation, PopSVG computes statistical significance of genes being population-level SVGs. Extensive experiments demonstrate PopSVG’s superiority over existing approaches in identifying biologically relevant SVGs and improving multi-slice tissue domain segmentation, while scaling efficiently to large datasets. A Python implementation of PopSVG is available at <a href="https://github.com/ToryDeng/PopSVG">https://github.com/ToryDeng/PopSVG</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

PopSVG enables scalable detection of spatially variable genes in population-level spatial transcriptomics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

查看全文
  • Tao Deng,
  • Zhe Yu,
  • Xiaobo Sun,
  • Tianwei Yu,
  • Hao Wu

摘要

Spatially variable gene (SVG) detection is one of the most important tasks in spatial transcriptomics (ST) data analysis. To identify SVGs from population-level ST data, current practices often call SVGs separately from individual slices and then combine, or select highly variable genes from the concatenated expression matrix of all subjects as a surrogate. These approaches fail to simultaneously account for the common and subject-specific spatial patterns, leading to low accuracy and power. To overcome this issue, we develop PopSVG, a statistical method for population-level SVG detection. PopSVG hierarchically models the spatial expression from a population of biological replicates, balancing inter-subject homogeneity and heterogeneity. After parameter estimation, PopSVG computes statistical significance of genes being population-level SVGs. Extensive experiments demonstrate PopSVG’s superiority over existing approaches in identifying biologically relevant SVGs and improving multi-slice tissue domain segmentation, while scaling efficiently to large datasets. A Python implementation of PopSVG is available at https://github.com/ToryDeng/PopSVG.