Joint Conditional Gaussian Graphical Models with Multiple Sources of Genomic Data

DSpace/Manakin Repository

Joint Conditional Gaussian Graphical Models with Multiple Sources of Genomic Data

Show full item record

Title: Joint Conditional Gaussian Graphical Models with Multiple Sources of Genomic Data
Author(s):
Chun, H.;
Chen, Min;
Li, B.;
Zhao, H.
Format: text
Item Type: article
Keywords: Gaussian graphical models
Genes
Facilitated sugar transporter
Joint sparsity
Description: Supplementary material included at the end of the article.
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.
ISSN: 1664-8021
Persistent Link: http://dx.doi.org/10.3389/fgene.2013.00294
http://hdl.handle.net/10735.1/3635
Terms of Use: ©2013 The Authors. CC-BY license.

Files in this item

Files Size Format View
NSM-FR-MChen-309486.71.pdf 787.2Kb PDF View/Open

This item appears in the following Collection(s)


Show full item record