Integration of Imaging (epi)Genomics Data for the Study of Schizophrenia Using Group Sparse Joint Nonnegative Matrix Factorization

作     者：Wang, Min Huang, Ting-Zhu Fang, Jian Calhoun, Vince D. Wang, Yu-Ping 

作者机构：Univ Elect Sci & Technol China Res Ctr Image & Vis Comp Sch Math Sci Chengdu 611731 Sichuan Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Univ New Mexico Mind Res Network Albuquerque NM 87131 USA 

出 版 物：《IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS》 (IEEE-ACM计算生物学与生物信息学汇刊)

年 卷 期：2020年第17卷第5期

页      面：1671-1681页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：973 Program [2013CB329404] NSFC NIH [R01GM109068, R01MH104680, R01MH107354, P20GM103472] NSF 

主　　题：Sparse matrices Biological system modeling DNA Functional magnetic resonance imaging Correlation Biomarkers Nonnegative matrix factorization group sparsity SNP fMRI methylation feature selection 

摘      要：Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI, and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to identify multi-dimensional modules. We also incorporate group structure information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with several brain activities. At the end, we have applied the method to the analysis of real data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to discover genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.