DNA microarrays are a new and promising biotechnology whichallows the monitoring of expression levels in cells for thousands of genes simultaneously. The present paper describes statistical methods for the identi cation of di erentially expressed genes in replicated cDNA microarray experiments. Although it is not the main focus of the paper, new methods for the important pre-processing steps of image analysis and normalization are proposed. Given suitably normalized data, the biological question of di erential expression is restated as a problem in multiple hypothesis testing: the simultaneous test for each geneof the null hypothesis of no association between the expression levels and responses or covariates of interest. Di erentially expressed genes are identi ed based on adjusted p-values for a multiple testing procedure which strongly controls the family-wise Type I error rate and takes into account the dependence structure between the gene expression levels. No speci c parametric form is assumed for the distribution of the test statistics and a permutation procedure is used to estimate adjusted p-values. Several data displays are suggested for the visual identi cation of di erentially expressed genes and of important features of these genes. The above methods are applied to microarray data from a study of gene expression in the livers of mice with very low HDL cholesterol levels. The genes identi ed using data from multiple slides are compared to those identi ed by recently published single-slide methods. Key words and phrases: Adjusted p-value, di erential gene expression, DNA microarray, image analysis, multiple testing, normalization, permutation test. 1.
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