The results of clustering the cells in source data and target data in four examples.

Note that the capital letters in the brackets represent the input data matrices for the corresponding methods: S represents source data, T and U represent the sub-matrices for the linked and unlinked features in target data, respectively. For integrative analysis methods (coupleCoC+, coupleCoC, Seurat, LIGER, scACE) that utilize both the source data and the target data as input, they produce clustering results of the cells in source data and target data simultaneously. We then summarize the clustering results by calculating ARI and NMI for source data and target data separately. For the remaining methods that are implemented on only one dataset, they produce clustering results of the cells in source data or target data independently. We then summarize the clustering results by calculating ARI and NMI for source data and target data separately. The source data type is scRNA-seq data for all four examples, while the target data types for examples 1–4 are scATAC-seq data, scRNA-seq data, sc-methylation data and scRNA-seq data, respectively. The symbol “-” means that the corresponding clustering method is not designed for that data type. We only compared the methods for integrative analysis of multiple datasets in example 4. nT and nS are the numbers of cells in the target data and the source data, correspondingly. Because we included the unlinked features when implementing CoC, k-means, Cusanovich2018, cisTopic, SC3, SIMLR and BPRMeth-G, the clustering results for these methods are better than that presented in [42].