Network-enabled gene expression analysis

BMC Bioinformatics

Methodology Article

Peter Sørensen¹  David Edwards¹  Lei Wang¹ 

[1] Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark;

关键词: Gene Expression Data;    Bayesian Network;    Conditional Independence;    Diquat;    Bayesian Network Model;   

DOI  :  10.1186/1471-2105-13-167

 received in 2011-11-16, accepted in 2012-06-28,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundAlthough genome-scale expression experiments are performed routinely in biomedical research, methods of analysis remain simplistic and their interpretation challenging. The conventional approach is to compare the expression of each gene, one at a time, between treatment groups. This implicitly treats the gene expression levels as independent, but they are in fact highly interdependent, and exploiting this enables substantial power gains to be realized.ResultsWe assume that information on the dependence structure between the expression levels of a set of genes is available in the form of a Bayesian network (directed acyclic graph), derived from external resources. We show how to analyze gene expression data conditional on this network. Genes whose expression is directly affected by treatment may be identified using tests for the independence of each gene and treatment, conditional on the parents of the gene in the network. We apply this approach to two datasets: one from a hepatotoxicity study in rats using a PPAR pathway, and the other from a study of the effects of smoking on the epithelial transcriptome, using a global transcription factor network.ConclusionsThe proposed method is straightforward, simple to implement, gives rise to substantial power gains, and may assist in relating the experimental results to the underlying biology.

【 授权许可】

CC BY   
© Edwards et al.; licensee BioMed Central Ltd. 2012