On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data

We consider the problem of inferring fold changes in gene expression from c DNA microarray data. Standard procedures focus on the ratio of measured flu orescent intensities at each spot on the microarray, but to do so is to ign ore the fact that the variation of such ratios is not constant. Estimates o f gene expression changes are derived within a simple hierarchical model th at accounts for measurement error and fluctuations in absolute gene express ion levels. Significant gene expression changes are identified by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.