Guided Co-clustering Transfer Across Unpaired and Paired Single-cell Multi-omics Data

Abstract

Single-cell multi-omics technologies enable the simultaneous profiling of gene expression and chromatin accessibility, providing complementary insights into cellular identity and gene regulatory mechanisms. However, integrating paired scRNA-seq and scATAC-seq data remains challenging due to inherent sparsity, technical noise, and the limited availability of high-quality paired measurements. In contrast, large-scale unpaired scRNA-seq datasets often exhibit robust and biologically meaningful cell cluster structures. We introduce Guided Co-clustering Transfer (GuidedCoC), a novel unsupervised framework that transfers structural knowledge from unpaired scRNA-seq source data to improve both cell clustering and feature alignment in paired scRNA-seq/scATAC-seq target data. GuidedCoC jointly co-clusters cells and features across modalities and domains via a unified information-theoretic objective, aligning gene expression modules with regulatory elements while implicitly performing cross-modal dimensionality reduction to reduce noise. Additionally, it automatically aligns cell types across unpaired and paired datasets without requiring explicit annotations. Extensive experiments on multiple benchmark datasets demonstrate that GuidedCoC achieves superior clustering accuracy and biological interpretability compared to existing methods. These results highlight the promise of structure-guided, unsupervised transfer learning for robust, scalable, and interpretable integration of single-cell multi-omics data. Code is available at https://github.com/No-AgCl/GuidedCoC.