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. 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. 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. This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. LANL LA-UR-02-4001.Īlso available in the e-Print archive and in Adobe Acrobat (.pdf) format. in A Practical Approach to Microarray Data Analysis. Rocha."Singular value decomposition and principal component analysis". Citation: Wall, Michael E., Andreas Rechtsteiner, Luis M.
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