SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection

• Published in: Genome Biology Vol. 26; no. 1; p. 135
• Main Authors: Chen, Mo, Cheng, Ruihua, He, Jianuo, Chen, Jun, Zhang, Jie
• Format: Journal Article
• Language: English
• Published: London BioMed Central 21.05.2025; Springer Nature B.V; BMC
• Subjects: Algorithms; Animal Genetics and Genomics; Bioinformatics; Biological analysis; Biomedical and Life Sciences; biomedical research; Cluster Analysis; Computational Biology - methods; data collection; Datasets; Discriminant analysis; domain; Evolutionary Biology; Gene expression; genome; Genomics - methods; Human Genetics; Humans; Life Sciences; Medical research; Method; Methods; Microbial Genetics and Genomics; Multiomics; Performance evaluation; Plant Genetics and Genomics; Principal Component Analysis; Principal components analysis; Simulation; Single-Cell Analysis; spatial data
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-025-03576-9

Technological advances have enabled us to profile multiple omics layers with spatial information, significantly enhancing spatial domain detection and advancing a variety of biomedical research fields. Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. Extensive experiments reveal that SMOPCA outperforms existing single-modal and multimodal dimension reduction and clustering methods, across both single-cell and spatial multi-omics datasets derived from diverse technologies and tissue structures.