A Summary of the Various Statistical Methods Currently in Use for the Analysis of Gene Expression Microarray Data

Microarrays generate a large volume of experimental data and many methods, such as clustering and classification, can be used to analyze this data. Initial investigation through exploratory analysis provides great insight into the data by detecting structures and patterns. After patterns have been identified, they can be used for both discovery and prediction of genes through various methods. Although pattern recognition methods incorporate some prior information, their use is limited because they do not provide an explanation for the underlying mechanism of genetic networks. Recently, many attempts have been made to use microarray data for uncovering these mechanisms. From initial investigative studies to developing genetic networks, this paper discusses an assortment of statistical methods that are currently being used to analyze gene expression microarray data.