Appropriate statistical design and analysis of gene expression microarray studies is critical in order to draw valid and useful conclusions from expression profiling studies of animal models. In this paper, several aspects of study design are discussed, including the number of animals that need to be studied to ensure sufficiently powered studies, usefulness of replication and pooling, and allocation of samples to arrays. Data preprocessing methods for both cDNA dual-label spotted arrays and Affymetrix-style oligonucleotide arrays are reviewed. High-level analysis strategies are briefly discussed for each of the types of study aims, namely class comparison, class discovery, and class prediction. For class comparison, methods are discussed for identifying genes differentially expressed between classes while guarding against unacceptably high numbers of false positive findings. Various clustering methods are discussed for class discovery aims. Class prediction methods are briefly reviewed, and reference is made to the importance of proper validation of predictors.