Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
Cao, Kai1,2; Hong, Yiguang1,3; Wan, Lin1,2
Source PublicationBIOINFORMATICS
AbstractMotivation: 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 framework 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 alignment 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.
Indexed BySCI
Funding ProjectNational Key R&D Program of China[2019YFA0709501] ; National Natural Science Foundation of China[61733018] ; National Natural Science Foundation of China[12071466] ; Shanghai Municipal Science and Technology Major Project[2021SHZDZX010] ; Fundamental Research Funds for the Central Universities ; LSC of CAS
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000736120000029
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Document Type期刊论文
Corresponding AuthorWan, Lin
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, LSC, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
3.Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China
Recommended Citation
GB/T 7714
Cao, Kai,Hong, Yiguang,Wan, Lin. Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona[J]. BIOINFORMATICS,2022,38(1):211-219.
APA Cao, Kai,Hong, Yiguang,&Wan, Lin.(2022).Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona.BIOINFORMATICS,38(1),211-219.
MLA Cao, Kai,et al."Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona".BIOINFORMATICS 38.1(2022):211-219.
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