SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data.

Authors

Jinxia Wang, Yuying Huo, Rui Zhao, Yan Pan, Jianqiang Wu, Han Wang, Xiangyu Li

Published in

PLoS biology. Volume 24. Issue 9. Pages e3003690. Sep 16, 2026. Epub Sep 16, 2026.

Abstract

Recent advances in spatial multi-omics technologies have opened new avenues for characterizing tissue architecture and function in situ, by simultaneously providing multimodal and complementary information-such as spatially resolved transcriptomic, epigenomic, and proteomic features. Current computational approaches face substantial challenges, such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images. To address this challenge, we proposed SpaMOAL (Spatially Multi-Omics graph contrAstive Learning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features, and molecular profiles, enabling accurate delineation of spatial tissue domains. Benchmarking across multiple recent paired spatial multi-omics datasets from mouse and human demonstrated that SpaMOAL consistently outperforms existing methods. By enabling accurate spatial domain delineation, SpaMOAL provides a powerful framework for interpreting tissue organization and cellular microenvironments.

PMID:
42748198
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.