Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. Most current clustering methods are designed for one data type only, such as scRNA-seq, scATAC-seq or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. Integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. We propose a coupled co-clustering-based unsupervised transfer learning algorithm ( couple CoC) for the integrative analysis of multimodal single-cell data. Our proposed couple CoC builds upon the information theoretic co-clustering framework. We applied couple CoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data, and scRNA-seq data from mouse and human. We demonstrate that couple CoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic data sets. The software and data sets are available at <https://github.com/cuhklinlab/coupleCoC>. ### Competing Interest Statement The authors have declared no competing interest.
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