RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2025 
FD July 01 
VO 35 
IS 7 
SP 1621 
OP 1632 
DO 10.1101/gr.279380.124 
UL http://genome.cshlp.org/content/35/7/1621.abstract 
AB 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.