Paper Info
Title
Analysis of gene sets based on the underlying regulatory network.
Abstract
Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast.
Year
DOI
Venue
2009
10.1089/cmb.2008.0081
JOURNAL OF COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
gene networks,gene set analysis,latent variable model,mixed linear model
Data mining,Gene,Linear model,Inference,A priori and a posteriori,Latent variable model,Correlation,Gene set analysis,Bioinformatics,Gene regulatory network,Mathematics
Journal
Volume
Issue
ISSN
16.0
3
1066-5277
Citations 
PageRank 
References 
17
0.96
6
Authors
2
Name
Order
Citations
PageRank
Shojaie, Ali1668.96
George Michailidis230335.19