Detecting Stable Clusters Using Principal Component Analysis

Clustering is one of the most commonly used tools in the analysis of gene expression data (1 ,2 ). The usage in grouping genes is based on the premise that coexpression is a result of coregulation. It is often used as a preliminary step in extracting gene networks and inference of gene function (3 ,4 ). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3 ,5 ,6 ), including sensitivity to drugs (7 ), and can also detect artifacts of experimental conditions (8 ). Clustering and its applications in biology are presented in greater detail in Chapter 13 (see also ref. 9 ). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well.