Unsupervised topological alignment for single-cell multi-omics integration
文献类型：期刊论文
| 作者 | Cao, Kai1,2; Bai, Xiangqi1,2; Hong, Yiguang1,2; Wan, Lin1,2 |
| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2020-07-01 |
| 卷号 | 36页码:48-56 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btaa443 |
| 英文摘要 | Motivation: 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. |
| 资助项目 | NSFC[11571349] ; NSFC[91630314] ; NSFC[61733018] ; NCMIS of CAS ; LSC of CAS ; Youth Innovation Promotion Association of CAS |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000579894600007 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/52313]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Wan, Lin |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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