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<p>Spatial domain detection methods often focus on high-variance structures, such as tumour-adjacent regions with sharp gene expression changes, while missing low-variance structures with subtle gene expression shifts, like those between adjacent normal and early adenoma regions. Here, to address this, we introduce ‘compare and contrast spatial transcriptomics’ (CoCo-ST), a graph contrastive feature representation framework. By comparing a target sample with a background sample, CoCo-ST detects both high-variance, broadly shared structures and low-variance, tissue-specific features. It offers technical advantages, including multisample integration, batch-effect correction and scalability across technologies from spot-level Visium data to single-cell Xenium Prime 5K and subcellular Visium HD data. We benchmarked CoCo-ST against ten state-of-the-art spatial-domain-detection algorithms using mouse lung precancerous samples, demonstrating its superior ability to identify low-variance spatial structures overlooked by other methods. CoCo-ST also effectively distinguishes cell clusters and niche structures in Visium HD and Xenium Prime 5K data. CoCo-ST is accessible at GitHub (<a href="https://github.com/WuLabMDA/CoCo-ST">https://github.com/WuLabMDA/CoCo-ST</a>).</p>

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CoCo-ST detects global and local biological structures in spatial transcriptomics datasets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月13日

查看全文
  • Muhammad Aminu,
  • Bo Zhu,
  • Natalie Vokes,
  • Hong Chen,
  • Lingzhi Hong,
  • Jianrong Li,
  • Junya Fujimoto,
  • Mehdi Chaib,
  • Yuqiu Yang,
  • Bo Wang,
  • Alissa Poteete,
  • Monique B. Nilsson,
  • Xiuning Le,
  • Tina Cascone,
  • David Jaffray,
  • Nicholas Navin,
  • Tao Wang,
  • Lauren A. Byers,
  • Don L. Gibbons,
  • John Heymach,
  • Ken Chen,
  • Chao Cheng,
  • Jianjun Zhang,
  • Jia Wu

摘要

Spatial domain detection methods often focus on high-variance structures, such as tumour-adjacent regions with sharp gene expression changes, while missing low-variance structures with subtle gene expression shifts, like those between adjacent normal and early adenoma regions. Here, to address this, we introduce ‘compare and contrast spatial transcriptomics’ (CoCo-ST), a graph contrastive feature representation framework. By comparing a target sample with a background sample, CoCo-ST detects both high-variance, broadly shared structures and low-variance, tissue-specific features. It offers technical advantages, including multisample integration, batch-effect correction and scalability across technologies from spot-level Visium data to single-cell Xenium Prime 5K and subcellular Visium HD data. We benchmarked CoCo-ST against ten state-of-the-art spatial-domain-detection algorithms using mouse lung precancerous samples, demonstrating its superior ability to identify low-variance spatial structures overlooked by other methods. CoCo-ST also effectively distinguishes cell clusters and niche structures in Visium HD and Xenium Prime 5K data. CoCo-ST is accessible at GitHub (https://github.com/WuLabMDA/CoCo-ST).