Unsupervised topological alignment for single-cell multi-omics integration

doi:10.1093/bioinformatics/btaa443

CSpace

	Unsupervised topological alignment for single-cell multi-omics integration
	Cao, Kai 1,2; Bai, Xiangqi 1,2; Hong, Yiguang 1,2; Wan, Lin 1,2
	2020-07-01
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	36 页码:48-56
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.
DOI	10.1093/bioinformatics/btaa443
收录类别	SCI
语种	英语
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
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000579894600007
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/52314
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.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, 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.