Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data

Although two-color fluorescent DNA microarrays are now standard equipment i n many molecular biology laboratories, methods for identifying differential ly expressed genes in microarray data are still evolving. Here, we report a refined test for differentially expressed genes which does not rely on gen e expression ratios but directly compares a series of repeated measurements of the two dye intensities for each gene. This test uses a statistical mod el to describe multiplicative and additive errors influencing an array expe riment, where model parameters are estimated from observed intensities for all genes using the method of maximum likelihood, A generalized likelihood ratio test is performed for each gene to determine whether, under the model , these intensities are significantly different. We use this method to iden tify significant differences in gene expression among yeast cells growing i n galactose-stimulating versus non-stimulating conditions and compare our r esults with current approaches for identifying differentially-expressed gen es. The effect of sample size on parameter optimization is also explored, a s is the use of the error model to compare the within- and between-slide in tensity variation intrinsic to an array experiment.