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

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ID: 325183
2026
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Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: The source code of scDAU is available at https://github.com/zhyu-lab/scdau and https://doi.org/10.5281/zenodo.19303337. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.
Reference Key
openalex_W7203618894 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jialiang Xue, Xiangmei Cao, Junlei Zhou, Yaowei Cao, Fangyuan Shi, Fang Du, Zhenhua Yu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag610
URL
https://doi.org/10.1093/bioinformatics/btag610
Keywords Keywords not found

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