Bioinformatics Advance Access originally published online on June 24, 2004
Bioinformatics 2004 20(17):3108-3127; doi:10.1093/bioinformatics/bth371
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Bioinformatics vol. 20 issue 17 © Oxford University Press 2004; all rights reserved.
Variance-modeled posterior inference of microarray data: detecting gene-expression changes in 3T3-L1 adipocytes
1 Department of Bioengineering, 2 Department of Medicine and 3 Department of Chemistry and Biochemistry, UC San Diego, La Jolla, CA 92093, USA
Received on November 10, 2004; accepted on May 17, 2004
Advance Access Publication June 24, 2004
Motivation: Microarrays are becoming an increasingly common tool for observing changes in gene expression over a large cross section of the genome. This experimental tool is particularly valuable for understanding the genome-wide changes in gene transcription in response to thiazolidinedione (TZD) treatment. The TZD class of drugs is known to improve insulin-sensitivity in diabetic patients, and is clinically used in treatment regimens. In cells, TZDs bind to and activate the transcriptional activity of peroxisome proliferator-activated receptor gamma (PPAR-
). Large-scale array analyses will provide some insight into the mechanisms of TZD-mediated insulin sensitization. Unfortunately, a theoretical basis for analyzing array data has not kept pace with the rapid adoption of this tool. The methods that are commonly used, particularly the fold-change approach and the standard t-test, either lack statistical rigor or resort to generalized statistical models that do not accurately estimate variability at low replicate numbers.
Results: We introduce a statistical framework that models the dependence of measurement variance on the level of gene expression in the context of a Bayesian hierarchical model. We compare several methods of parameter estimation and subsequently apply these to determine a set of genes in 3T3-L1 adipocytes that are differentially regulated in response to TZD treatment. When the number of experimental replicates is low (n = 2–3), this approach appears to qualitatively preserve an equivalent degree of specificity, while vastly improving sensitivity over other comparable methods. In addition, the statistical framework developed here can be readily applied to understand the implicit assumptions made in traditional fold-change approaches to array analysis.
Availability: Our statistical approach is implemented as a set of Java-based tools called VAMPIRE, accessible from our website at http://genome.ucsd.edu/VAMPIRE
Contact: shankar{at}ucsd.edu
* To whom correspondence should be addressed.
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