title: Joint conditional Gaussian graphical models with multiple sources of genomic data.
abstract: It is challenging to identify meaningful gene networks because biological interactions are often condition-specific and confounded with external factors. It is necessary to integrate multiple sources of genomic data to facilitate network inference. For example, one can jointly model expression datasets measured from multiple tissues with molecular marker data in so-called genetical genomic studies. In this paper, we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological processes based on multiple sources of data. This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization. We apply our approach to a real dataset measuring gene expression in four tissues (kidney, liver, heart, and fat) from recombinant inbred rats. Our approach reveals that the liver tissue has the highest level of tissue-specific gene regulations among genes involved in insulin responsive facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.
authors.affiliation: Department of Statistics, Purdue University West Lafayette, IN, USA.
authors.forename: Hyonho
authors.initials: H
authors.lastname: Chun
authors.affiliation: Department of Mathematical Sciences, University of Texas at Dallas Dallas, TX, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Chen
authors.affiliation: Department of Statistics, The Pennsylvania State University, University Park PA, USA.
authors.forename: Bing
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Biostatistics, Yale School of Public Health New Haven, CT, USA.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
keywords: GGMs; Gaussian graphical models; conditional GGMs; gene networks; joint sparsity