Self-Organizing Maps
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Microarrays are a powerful experimental platform, allowing simultaneous studies of gene expression for thousands of genes under different experimental conditions. However there is much biological variability induced throughout the experimental process that can obscure the biological signals of interest. As such, the need for experimental design, replication and statistical rigor are now widely recognized. Statistical hypothesis testing has become the accepted differential expression analysis approach and many classification and prediction methods used in class discovery and class prediction now incorporate stochastic modeling components.This paper provides a review of statistical analysis approaches to the analysis of data from microarray experiments. This includes discussion of experimental design, data management, preprocessing, differential expression, clustering and class prediction, reporting and annotation. The review is illustrated with the analysis of an experiment with 3 experimental conditions using the Affymetrix murine chip mgu 74av2; and with descriptions of available functionality in the statistical analysis software S-PLUS and its associated module for microarray analysis, S+ArrayAnalyzer.