A Practical Approach to Microarray Data Analysis

Abstract

This chapter describes gene expression analysis by Singular ValueDecomposition (SVD), emphasizing initial characterization of thedata. We describe SVD methods for visualization of gene expressiondata, representation of the data using a smaller number of variables,and detection of patterns in noisy gene expression data. In addition,we describe the precise relation between SVD analysis and PrincipalComponent Analysis (PCA) when PCA is calculated using the covariancematrix, enabling our descriptions to apply equally well to eithermethod. Our aim is to provide definitions, interpretations, examples,and references that will serve as resources for understanding andextending the application of SVD and PCA to gene expression analysis.