A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis

doi:10.1109/TNNLS.2021.3054635

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis
	Min, Wenwen 1,2,3; Wan, Xiang 1; Chang, Tsung-Hui 1,3; Zhang, Shihua 4,5,6,7
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-02-08
页码	15
关键词	Gene expression Biology Biological system modeling Principal component analysis Mathematical model Data models Big Data Absolute-valued graph regularization graph regularization sparse learning structured sparse singular value decomposition (SVD)
ISSN号	2162-237X
DOI	10.1109/TNNLS.2021.3054635
英文摘要	Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal. However, this model ignores the structural information between variables (e.g., a gene network). The typical graph-regularized penalty can be used to incorporate such prior graph information to achieve more accurate discovery and better interpretability. However, the existing approach fails to consider the opposite effect of variables with negative correlations. In this article, we propose a novel sparse graph-regularized SVD model with absolute operator (AGSVD) for high-dimensional gene expression pattern discovery. The key of AGSVD is to impose a novel graph-regularized penalty (\|u\|TL\|u\|). However, such a penalty is a nonconvex and nonsmooth function, so it brings new challenges to model solving. We show that the nonconvex problem can be efficiently handled in a convex fashion by adopting an alternating optimization strategy. The simulation results on synthetic data show that our method is more effective than the existing SVD-based ones. In addition, the results on several real gene expression data sets show that the proposed methods can discover more biologically interpretable expression patterns by incorporating the prior gene network.
资助项目	National Science Foundation of China[61802157] ; National Science Foundation of China[61731018] ; National Science Foundation of China[61621003] ; National Science Foundation of China[1661141019] ; Natural Science Foundation of Jiangxi Province of China[20192BAB217004] ; China Postdoctoral Science Foundation[2020M671902] ; Open Research Fund from Shenzhen Research Institute of Big Data[2019ORF01002] ; National Key Research and Development Program of China[2019YFA0709501] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSWSYS008] ; National Ten Thousand Talent Program for Young Top-notch Talents
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000732256400001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/59724]
专题	应用数学研究所
通讯作者	Zhang, Shihua
作者单位	1.Shenzhen Res Inst Big Data, Shenzhen 518172, Peoples R China 2.Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Peoples R China 3.Chinese Univ Hong Kong, Sch Sci & Engn, Shenzhen 518172, Peoples R China 4.Chinese Acad Sci, Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Key Lab Syst Biol, Hangzhou 310024, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China 6.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 7.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Min, Wenwen,Wan, Xiang,Chang, Tsung-Hui,et al. A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15.
APA	Min, Wenwen,Wan, Xiang,Chang, Tsung-Hui,&Zhang, Shihua.(2021).A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA	Min, Wenwen,et al."A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15.