Unsupervised topological alignment for single-cell multi-omics integration
Cao, Kai1,2; Bai, Xiangqi1,2; Hong, Yiguang1,2; Wan, Lin1,2
Source PublicationBIOINFORMATICS
AbstractMotivation: Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. Results: In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex non-linear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features.
Indexed BySCI
Funding ProjectNSFC[11571349] ; NSFC[91630314] ; NSFC[61733018] ; NCMIS of CAS ; LSC of CAS ; Youth Innovation Promotion Association 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:000579894600007
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Document Type期刊论文
Corresponding AuthorWan, Lin
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Cao, Kai,Bai, Xiangqi,Hong, Yiguang,et al. Unsupervised topological alignment for single-cell multi-omics integration[J]. BIOINFORMATICS,2020,36:48-56.
APA Cao, Kai,Bai, Xiangqi,Hong, Yiguang,&Wan, Lin.(2020).Unsupervised topological alignment for single-cell multi-omics integration.BIOINFORMATICS,36,48-56.
MLA Cao, Kai,et al."Unsupervised topological alignment for single-cell multi-omics integration".BIOINFORMATICS 36(2020):48-56.
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