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

Authors

Yucheng Lu, Xun Zhang, Hongwei Li

Published in

Quantitative biology (Beijing, China). Volume 14. Issue 3. Pages e70041. Epub May 06, 2026.

Abstract

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.

PMID:
42845644
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.

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