scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration

TL;DR

scMRDR is a scalable, flexible framework that effectively integrates unpaired multi-omics single-cell data by disentangling shared and modality-specific features, preserving biological signals, and supporting large datasets.

Contribution

It introduces a novel disentangled representation approach with regularization and adversarial training for unpaired multi-omics integration, scalable to large datasets and multiple modalities.

Findings

Achieves superior batch correction and modality alignment on benchmarks.

Effectively preserves biological heterogeneity.

Scales to large datasets and integrates more than two omics.

Abstract

Advances in single-cell sequencing have enabled high-resolution profiling of diverse molecular modalities, while integrating unpaired multi-omics single-cell data remains challenging. Existing approaches either rely on pair information or prior correspondences, or require computing a global pairwise coupling matrix, limiting their scalability and flexibility. In this paper, we introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration. Specifically, we disentangle each cell's latent representations into modality-shared and modality-specific components using a well-designed $\beta$-VAE architecture, which are augmented with isometric regularization to preserve intra-omics biological heterogeneity, adversarial objective to encourage cross-modal alignment, and masked reconstruction loss strategy to address the issue of missing features across modalities. Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation. Crucially, it scales effectively to large-scale datasets and supports integration of more than two omics, offering a powerful and flexible solution for large-scale multi-omics data integration and downstream biological discovery.