scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data

Cross-modal translation enables reconstruction of missing modalities in single-cell multi-omics data, supporting integrative analyses of cellular heterogeneity and regulatory relationships. However, existing methods often struggle to disentangle shared biological signals from modality-specific variation and to generalize across datasets.

We present scDAU, a deep learning framework that combines conditional diffusion-based feature regularization with multi-scale cross-modal translation networks. scDAU employs a feature decoupling strategy to separate shared semantic representations from modality-specific components, followed by U-Net-based architectures for accurate bidirectional translation between modalities. Across multiple benchmark datasets, scDAU outperforms existing methods in both within-dataset and cross-dataset settings, as well as in predicting modalities for previously unseen cell types. The framework further generalizes to transcriptome-proteome translation, demonstrating flexibility across diverse multi-omics contexts. Application to a human glioblastoma dataset showed that scDAU preserves cell-type-specific gene expression and chromatin accessibility patterns, supporting downstream analyses such as marker identification and functional enrichment. Overall, scDAU provides a robust and extensible approach for cross-modal translation.

The source code of scDAU is available at https://github.com/zhyu-lab/scdau and https://doi.org/10.5281/zenodo.19303337.

Supplementary data is available at Bioinformatics online.