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Mol Microbiol. 2003 Feb;47(4):871-7.

Differential analysis of DNA microarray gene expression data.

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  • 1Department of Microbiology, Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine, CA 92697, USA.


Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.

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