January 28 @ 4:30 PM - 5:30 PM - Smith Hall 108
Title: "Joint Conditional Gaussian Graphical Models with Multiple Sources of Genomic Data"
Speaker: Hyonho Chun, Department of Statistics, Purdue University
Place: SMITH (SMTH) Hall 108
Date: January 28, 2014; Tuesday
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 responsiv!
e facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.
1. H Chun, M Chen, B Li and H Zhao (2013) Joint conditional Gaussian graphical models with multiple sources of genomic data, Frontiers in Genetics-Bioinformatics and Computational Biology.
2. B Li, H Chun and H Zhao (2012) Sparse Estimation of Conditional Graphical Models with Application to Gene Networks, Journal of the American Statistical Association 107.