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.