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Statistical Techniques for Microarray Data: A Partial Overview

Abstract Modern microarray technology has enabled biologists to record the expression profiles of thousands of genes in a single experiment. The large volume of data generated from these experiment (many available on public internet sites) has created tremendous opportunities for the statisticians to get involved in this exciting development and create appropriate statistical tools for analyzing these data. We have attempted to present an overview of some of the major statistical developments in this area, such as statistical clustering and classification, normalization and correction for systematic bias, empirical Bayes detection of differential gene expression, ANOVA models and exploration of relationships of expression levels between genes. The scope of such an overview is “partial” by nature because novel papers dealing with statistical analysis of microarray data are being added to the literature at an increasing pace.