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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\n                    <jats:italic>couple<\/jats:italic>\n                    CoC+ for the integrative analysis of single-cell genomic data.\n                    <jats:italic>couple<\/jats:italic>\n                    CoC+ is a transfer learning method based on the information-theoretic co-clustering framework. In\n                    <jats:italic>couple<\/jats:italic>\n                    CoC+, we utilize the information in one dataset, the source data, to facilitate the analysis of another dataset, the target data.\n                    <jats:italic>couple<\/jats:italic>\n                    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,\n                    <jats:italic>couple<\/jats:italic>\n                    CoC+ matches similar cell types across the source data and the target data. By applying\n                    <jats:italic>couple<\/jats:italic>\n                    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\n                    <jats:italic>couple<\/jats:italic>\n                    CoC+ improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets.\n                    <jats:italic>couple<\/jats:italic>\n                    CoC+ has fast convergence and it is computationally efficient. The software is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/cuhklinlab\/coupleCoC_plus\" xlink:type=\"simple\">https:\/\/github.com\/cuhklinlab\/coupleCoC_plus<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009064","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T01:12:50Z","timestamp":1622682770000},"page":"e1009064","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":20,"title":["coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic 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