Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
- Jianing Yao1,
- Jinglun Yu2,
- Brian Caffo1,
- Stephanie C. Page3,
- Keri Martinowich3,4,5 and
- Stephanie C. Hicks1,6,7,8
- 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA;
- 2Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
- 3Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland 21205, USA;
- 4The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA;
- 5Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA;
- 6Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;
- 7Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland 21218, USA;
- 8Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland 21218, USA
Abstract
Recent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and cell types provide multiple perspectives and a more complete understanding of tissue architecture. Here, we introduce Proust, a scalable tool to predict discrete domains using spatial multi-omics data by combining the low-dimensional representation of biological profiles based on graph-based contrastive self-supervised learning. Our scalable method integrates multiple data modalities, such as RNA, protein, and H&E images, and predicts spatial domains within tissue samples. Through the integration of multiple modalities, Proust consistently demonstrates enhanced accuracy in detecting spatial domains, as evidenced across various benchmark data sets and technological platforms.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279380.124.
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Freely available online through the Genome Research Open Access option.
- Received March 19, 2024.
- Accepted May 14, 2025.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
