coupleCoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data

Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and sc-methylation data, usually have different powers in identifying the unknown cell types through clustering. So, methods that integrate multiple datasets can potentially lead to a better clustering performance. Here we propose couple CoC+ for the integrative analysis of single-cell genomic data. couple CoC+ is a transfer learning method based on the information-theoretic co-clustering framework. In couple CoC+, we utilize the information in one dataset, the source data, to facilitate the analysis of another dataset, the target data. couple CoC+ uses the linked features in the two datasets for effective knowledge transfer, and it also uses the information of the features in the target data that are unlinked with the source data. In addition, couple CoC+ matches similar cell types across the source data and the target data. By applying couple CoC+ to the integrative clustering of mouse cortex scATAC-seq data and scRNA-seq data, mouse and human scRNA-seq data, mouse cortex sc-methylation and scRNA-seq data, and human blood dendritic cells scRNA-seq data from two batches, we demonstrate that couple CoC+ improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. couple CoC+ has fast convergence and it is computationally efficient. The software is available at https://github.com/cuhklinlab/coupleCoC_plus .