AUTHOR=Chun Hyonho , Chen Min , Li Bing , Zhao Hongyu 

TITLE=Joint conditional Gaussian graphical models with multiple sources of genomic data

JOURNAL=Frontiers in Genetics

VOLUME=Volume 4 - 2013

YEAR=2013

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2013.00294

DOI=10.3389/fgene.2013.00294

ISSN=1664-8021

ABSTRACT=It is challenging to identify meaningful gene networks  because biological<br/>interactions are often condition-specific and confounded with external<br/>factors. It is necessary to integrate multiple sources of genomic<br/>data to facilitate network inference. For example,  one can jointly<br/>model expression datasets measured from multiple tissues with molecular<br/>marker data in so-called genetical genomic studies. In this paper,<br/>we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological<br/>processes based on multiple sources of data. This approach is able<br/>to integrate  multiple sources of information by adopting conditional<br/>models combined with joint sparsity regularization.  We apply our approach <br/>to a real dataset measuring gene expression in four tissues<br/>(kidney, liver, heart and fat) from recombinant inbred rats.  Our<br/>approach reveals that the liver tissue has the highest level of<br/>tissue-specific gene regulations among genes involved in  {\it 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.<br/>