  This chapter describes gene expression analysis by Singular Value
Decomposition (SVD), emphasizing initial characterization of the data. We
describe SVD methods for visualization of gene expression data, 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 Principal Component Analysis (PCA) when PCA is
calculated using the covariance matrix, enabling our descriptions to apply
equally well to either method. Our aim is to provide definitions,
interpretations, examples, and references that will serve as resources for
understanding and extending the application of SVD and PCA to gene expression
analysis.
