scMOG: A graph neural network method for regulatory relationship‐preserving single‐cell multi‐omics integration

Single‐cell multi‐omics sequencing technology provides a powerful tool for studying cellular heterogeneity. However, beyond the challenges of sparsity, heterogeneity, and dimensionality differences, a critical challenge in multi‐omics data integration lies in preserving the true regulatory relationships among molecular features. To address these limitations, we propose single‐cell multi‐omics graph neural networks (scMOG), a framework that leverages heterogeneous graphs to preserve regulatory relationships in single‐cell multi‐omics data. scMOG leverages encoders to extract low‐dimensional embeddings of both cells and features while reconstructing the input data using zero‐inflated negative binomial decoders, effectively handling high sparsity and noise. In addition, scMOG introduces a contrastive learning module and an omics alignment module to preserve differences in expression patterns across distinct omics while extracting consistent information. Experimental results on eight single‐cell multi‐omics datasets demonstrate that scMOG outperforms existing methods, producing embeddings that capture meaningful biological signals. scMOG provides an effective solution for integrating single‐cell multi‐omics data, offering a scalable framework that preserves regulatory signals.