  DNA microarrays are a relatively new technology that can simultaneously
measure the expression level of thousands of genes. They have become an
important tool for a wide variety of biological experiments. One of the most
common goals of DNA microarray experiments is to identify genes associated with
biological processes of interest. Conventional statistical tests often produce
poor results when applied to microarray data due to small sample sizes, noisy
data, and correlation among the expression levels of the genes. Thus, novel
statistical methods are needed to identify significant genes in DNA microarray
experiments. This article discusses the challenges inherent in DNA microarray
analysis and describes a series of statistical techniques that can be used to
overcome these challenges. The problem of multiple hypothesis testing and its
relation to microarray studies is also considered, along with several possible
solutions.
