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

Journal of Data Science

article

Yunpeng Zhao¹  Liuqing Yang²  Qing Pan² 

[1] School of Mathematical and Natural Sciences, Arizona State University;Department of Statistics, George Washington University

关键词: EM algorithm;    empirical guidelines;    microarray data;    normal hierarchical model;    single cell RNAseq;    stochastic block model;   

DOI  :  10.6339/21-JDS1028

学科分类：土木及结构工程学

来源: JDS

【 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.

【 授权许可】

CC BY