Cluster Assisted Mixed effect Models (CAMM) for the identification of differentially expressed genes

With many of the major technological issues overcome, the remaining hurdle to the widespread use of microarrays is analytical. With so many genes and often very few replicates, most commonly used statistical techniques are inadequate to the task. Here we present a new technique for identifying differentially expressed genes based on linear mixed effect models and cluster analysis, that combines the advantages of univariate and multivariate methods whilst beginning to take into account the correlation structure of the data. Our results with the Golub leukemia data set have been promising, with our method selecting not only the commonly identified important genes, but several other biologically significant genes as well.