Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

作     者：Xiaoxi Hu Yue Ma Yakun Xu Peiyao Zhao Jun Wang Xiaoxi Hu;Yue Ma;Yakun Xu;Peiyao Zhao;Jun Wang

作者机构：CAS Key Laboratory of Pathogenic Microbiology and ImmunologyInstitute of MicrobiologyChinese Academy of SciencesBeijing 100101China Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreMD 21205USA University of Chinese Academy of SciencesBeijing 100049China Department of BiostatisticsUniversity of MichiganAnn ArborMI 48109-2029USA 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2021年第7卷第12期

页      面：1725-1731页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 07[理学] 08[工学] 

基　　金：the National Key Research and Development Program of China(2018YFC2000500) the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB29020000) the National Natural Science Foundation of China(31771481 and 91857101) the Key Research Program of the Chinese Academy of Sciences(KFZD-SW-219),“China Microbiome Initiative.” 

主　　题：Multivariate regression methods Reduced rank regression Sparsity Dimensionality reduction Variable selection 

摘      要：Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression *** traditional multivariate approaches such as principal component analysis have been used broadly in various research areas,including investment analysis,image identification,and population genetic structure ***,these common approaches have the limitations of ignoring the correlations between responses and a low variable selection ***,in this article,we introduce the reduced rank regression method and its extensions,sparse reduced rank regression and subspace assisted regression with row sparsity,which hold potential to meet the above demands and thus improve the interpretability of regression *** conducted a simulation study to evaluate their performance and compared them with several other variable selection *** different application scenarios,we also provide selection suggestions based on predictive ability and variable selection ***,to demonstrate the practical value of these methods in the field of microbiome research,we applied our chosen method to real population-level microbiome data,the results of which validated our *** method extensions provide valuable guidelines for future omics research,especially with respect to multivariate regression,and could pave the way for novel discoveries in microbiome and related research fields.