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

Authors

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

Published in

Nature communications. Jul 01, 2026. Epub Jul 01, 2026.

Abstract

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.

PMID:
42386773
Bibliographic data and abstract were imported from PubMed on 02 Jul 2026.

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