Motivation: Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure. 

　　Results: In this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment frame- work that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold align- ment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. We evaluated Pamona on a comprehensive set of publicly available benchmark datasets. We demonstrated that Pamona can accurately identify shared and dataset- specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in a common space, outperforming the comparable existing methods. 

　　Availability and implementation: Pamona software is available at https://github.com/caokai1073/Pamona. 

　　Contact: lwan@amss.ac.cn 

　　Supplementary information: Supplementary data are available at Bioinformatics online. 

　　Publication: 

　　Bioinformatics, Volume 38, Issue 1, 1 January 2022, Pages 211–219.

　　Author:  

　　Kai Cao  

　　LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences 

　　School of Mathematical Sciences, University of Chinese Academy of Sciences 

　　Yiguang Hong  

　　LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences 

　　Department of Control Science and Engineering, Tongji University 

　　Lin Wan  

　　LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences 

　　School of Mathematical Sciences, University of Chinese Academy of Sciences