Effective and Robust Single-cell Cross-omics Annotation via Vector-Quantized Autoencoders

Abstract

Integrating unpaired single-cell RNA-seq and ATAC-seq data remains challenging due to the profound differences in data sparsity and noise between them. This paper presents a novel discrete representation learning based method for effective and robust cross-omics cell-type annotation, which is called scCoA-VQA --- the abbreviation of single-cell Cross-omics Annotation via Vector-Quantized Autoencoders. scCoA-VQA employs omics-specific vector-quantized autoencoders to construct stable discrete latent spaces, and aligns RNA and ATAC representations via an anchor-constrained autoencoder constrained by biologically meaningful intra- and inter-omics anchors. A two-phase label transfer strategy is proposed to achieve accurate label transfer by combining inter-omics propagation with intra-ATAC refinement. Extensive experiments on both synthetic and real-world datasets show that scCoA-VQA consistently outperforms existing methods in accuracy, F1-Macro, and robustness to extreme sparsity. Further biological analysis demonstrate that scCoA-VQA can accurately capture the meaningful regulatory structure, including transitional Naive–Effector T-cell states and Naive-like versus Memory-like epigenetic subpopulations in CD4 TCM cells. These results indicate that discrete, anchor-constrained latent modeling provides a powerful and biologically coherent solution for unpaired single-cell multi-omics integration. Source code of this work is available at https://github.com/penghan-ph/scCoA-VQA/.