Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies

• Published in: Genome research Vol. 35; no. 7; pp. 1621 - 1632
• Main Authors: Yao, Jianing, Yu, Jinglun, Caffo, Brian, Page, Stephanie C., Martinowich, Keri, Hicks, Stephanie C.
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.07.2025
• Subjects: Biological analysis; Computational Biology - methods; Genomics - methods; Humans; Immunofluorescence; Method; Multiomics; Proteomics - methods; Spatial discrimination learning; Supervised Machine Learning
• ISSN: 1088-9051; 1549-5469; 1549-5469
• DOI: 10.1101/gr.279380.124

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.