Identification of significant features in DNA microarray data.
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 owing 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 are also considered, along with several possible solutions.
Department of Endodontics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA ; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Eric
E
Bair
feature selection; genetics; microarray; multiple testing