Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types

doi:10.1093/bib/bbab546

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types
	Wu, Wenming 1; Zhang, Wensheng2,3,4 ; Ma, Xiaoke 1,5
发表期刊	BRIEFINGS IN BIOINFORMATICS
ISSN	1467-5463
	2022-03-10
卷号	23 期号:2 页码:13
通讯作者	Ma, Xiaoke(xkma@xidian.edu.cn)
摘要	Advances in single-cell biotechnologies simultaneously generate the transcriptomic and epigenomic profiles at cell levels, providing an opportunity for investigating cell fates. Although great efforts have been devoted to either of them, the integrative analysis of single-cell multi-omics data is really limited because of the heterogeneity, noises and sparsity of single-cell profiles. In this study, a network-based integrative clustering algorithm (aka NIC) is present for the identification of cell types by fusing the parallel single-cell transcriptomic (scRNA-seq) and epigenomic profiles (scATAC-seq or DNA methylation). To avoid heterogeneity of multi-omics data, NIC automatically learns the cell-cell similarity graphs, which transforms the fusion of multi-omics data into the analysis of multiple networks. Then, NIC employs joint non-negative matrix factorization to learn the shared features of cells by exploiting the structure of learned cell-cell similarity networks, providing a better way to characterize the features of cells. The graph learning and integrative analysis procedures are jointly formulated as an optimization problem, and then the update rules are derived. Thirteen single-cell multi-omics datasets from various tissues and organisms are adopted to validate the performance of NIC, and the experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods in terms of various measurements. The proposed algorithm provides an effective strategy for the integrative analysis of single-cell multi-omics data (The software is coded using Matlab, and is freely available for academic ).
关键词	single-cell multi-omics data integrative analysis adaptive graph learning cell type
DOI	10.1093/bib/bbab546
关键词[WOS]	HETEROGENEITY ; DYNAMICS
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018AAA0102100] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61772394] ; Key Research and Development Program of Shaanxi[2021ZDLGY02-02] ; Key Research and Development Program of Gansu[21YF5GA063]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research and Development Program of Shaanxi ; Key Research and Development Program of Gansu
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000804196500065
出版者	OXFORD UNIV PRESS
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49636
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Ma, Xiaoke
作者单位	1.Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, Machine Learning & Data Min, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res & Dev Dept, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Univ Iowa, Iowa City, IA 52242 USA
推荐引用方式 GB/T 7714	Wu, Wenming,Zhang, Wensheng,Ma, Xiaoke. Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types[J]. BRIEFINGS IN BIOINFORMATICS,2022,23(2):13.
APA	Wu, Wenming,Zhang, Wensheng,&Ma, Xiaoke.(2022).Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types.BRIEFINGS IN BIOINFORMATICS,23(2),13.
MLA	Wu, Wenming,et al."Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types".BRIEFINGS IN BIOINFORMATICS 23.2(2022):13.