Integrative Clustering Analysis with Application in Multi-Source Gene Expression Data

作     者：Yang, Liuqing Pan, Qing Zhao, Yunpeng 

作者机构：Department of Statistics George Washington University Washington DC United States School of Mathematical and Natural Sciences Arizona State University Tempe AZ United States 

出 版 物：《Journal of Data Science》 (J. Data Sci.)

年 卷 期：2022年第20卷第1期

页      面：14-33页

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 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）] 

主　　题：EM algorithm empirical guidelines microarray data normal hierarchical model single cell RNAseq stochastic block model 

摘      要：In omics studies, different sources of information about the same set of genes are often available. When the group structure (e.g., gene pathways) within the genes are of interests, we combine the normal hierarchical model with the stochastic block model, through an integrative clustering framework, to model gene expression and gene networks jointly. The integrative framework provides higher accuracy in extensive simulation studies when one or both of the data sources contain noises or when different data sources provide complementary information. An empirical guideline in the choice between integrative versus separate clustering models is proposed. The integrative clustering method is illustrated on the mouse embryo single cell RNAseq and bulk cell microarray data, which identified not only the gene sets shared by both data sources but also the gene sets unique in one data source. © 2022 Center for Applied Statistics, School of Statistics, Renmin University of China. All rights reserved.