Thu, 21 Aug 2008 15:11:59 BST CiteULike: AbnerCYH's Rocha http://www.citeulike.org/user/AbnerCYH/author/Rocha CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/AbnerCYH/article/352522 <i>(3 Mar 2003)</i><br /><br />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. Singular Value Decomposition and Principal Component Analysis Michael Wall Andreas Rechtsteiner Luis Rocha (3 Mar 2003) 2005-10-17T02:36:40-00:00 2003 algebra algorithms kdd