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<p>Spatial multi-omics technologies revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that data from different omics share a common underlying distribution and aim to project them into a single unified latent space. This assumption, however, obscures the unique insights offered by each omics, thereby limiting the full potential of multi-omics analyses. To address this limitation, we develop the Spatial Multi-View (SpaMV) representation learning algorithm, which explicitly captures both shared information across omics and the distinct, omics-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior spatial domain clustering performance and offers topic modeling with more interpretable dimensionality reduction for downstream analysis. Moreover, our method more effectively discovers interpretable omics-specific biomarkers than existing approaches, highlighting its strength in disentangling multi-omics signals.</p>

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Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Yang Liu,
  • Kexin Ma,
  • Haoran Xu,
  • Ke Xu,
  • Yunfei Hu,
  • Zhenhan Lin,
  • Jiangli Lin,
  • Bo Han,
  • Shuaicheng Li,
  • Zhixiang Lin,
  • Xin Maizie Zhou,
  • Lu Zhang

摘要

Spatial multi-omics technologies revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that data from different omics share a common underlying distribution and aim to project them into a single unified latent space. This assumption, however, obscures the unique insights offered by each omics, thereby limiting the full potential of multi-omics analyses. To address this limitation, we develop the Spatial Multi-View (SpaMV) representation learning algorithm, which explicitly captures both shared information across omics and the distinct, omics-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior spatial domain clustering performance and offers topic modeling with more interpretable dimensionality reduction for downstream analysis. Moreover, our method more effectively discovers interpretable omics-specific biomarkers than existing approaches, highlighting its strength in disentangling multi-omics signals.