SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data
5 affiliations
- 1School of Software Engineering, Beijing Jiaotong University, Beijing, China
- 2Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
- 3School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- 4National Infrastructure for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- 5College of Science, China Agricultural University, Beijing, China
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.
Spatial multi-omics technologies reveal tissue organization through diverse molecular and spatial data, yet integrating these multi-omics and histology datasets remains challenging. This study develops SpaMOAL, a graph contrastive learning framework that enables accurate spatial domain identification by integrating diverse spatial multi-omics data.
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BibTeX
@article{wang2026spamoal,
title = {{SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data}},
author = {Jinxia Wang and Yuying Huo and Rui Zhao and Yan Pan and Jianqiang Wu and Han Wang and Xiangyu Li},
journal = {PLOS Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pbio.3003690},
url = {https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003690},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SpaMOAL is a deep learning method that enables accurate spatial domain identification from multi-omics data
AU - Jinxia Wang
AU - Yuying Huo
AU - Rui Zhao
AU - Yan Pan
AU - Jianqiang Wu
AU - Han Wang
AU - Xiangyu Li
PY - 2026
DA - 2026/09/16
JO - PLOS Biology
DO - 10.1371/journal.pbio.3003690
UR - https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003690
AB - 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. Spatial multi-omics technologies reveal tissue organization through diverse molecular and spatial data, yet integrating these multi-omics and histology datasets remains challenging. This study develops SpaMOAL, a graph contrastive learning framework that enables accurate spatial domain identification by integrating diverse spatial multi-omics data.
SN - 1545-7885
ER -