Network-enabled gene expression analysis.
BACKGROUND
Although 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.


RESULTS
We 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.


CONCLUSIONS
The 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.
Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark. David.Edwards@agrsci.dk
David
D
Edwards
Lei
L
Wang
Peter
P
Sørensen
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D008099:Liver / Q000187:drug effects / Q000378:metabolism; D008297:Male; D053858:Metabolic Networks and Pathways; D047492:Peroxisome Proliferator-Activated Receptors / Q000378:metabolism; D051381:Rats; D014157:Transcription Factors / Q000378:metabolism
D047492:Peroxisome Proliferator-Activated Receptors; D014157:Transcription Factors