Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
- Jianing Yao1,
- Jinglun Yu2,
- Brian Caffo1,
- Stephanie C Page3,
- Keri Martinowich4 and
- Stephanie C Hicks5,6
- 1 Johns Hopkins Bloomberg School of Public Health;
- 2 Johns Hopkins University;
- 3 Lieber Institute for Brain Development, Johns Hopkins Medical Campus;
- 4 Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Johns Hopkins School of Medicine;
- 5 Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University
Abstract
Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or 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 datasets and technological platforms.
- Received March 19, 2024.
- Accepted May 14, 2025.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International license), as described at http://creativecommons.org/licenses/by/4.0/.
