TY  - JOUR
A1  - Yao, Jianing
A1  - Yu, Jinglun
A1  - Caffo, Brian
A1  - Page, Stephanie C.
A1  - Martinowich, Keri
A1  - Hicks, Stephanie C.
T1  - Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
Y1  - 2025/07/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1621 
EP  - 1632 
DO  - 10.1101/gr.279380.124 
VL  - 35 
IS  - 7 
UR  - http://genome.cshlp.org/content/35/7/1621.abstract 
N2  - 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. 
ER  -