Principal component analysis microarray data

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A whole spectrum of statistical techniques have been applied to the analysis of DNA microarray data [26-28]. These include clustering analysis (hierarchical, K-means, self-organizing maps), dimension reduction (singular value decomposition, principal component analysis, multidimensional scaling, or correspondence analysis), and supervised classification (support vector machines, artificial neural networks, discriminant methods, or between-group analysis) methods. More recently, a number of Bayesian and other probabilistic approaches have been employed in the analysis of DNA microarray data [11], Generally, the first phase of microarray data analysis is exploratory data analysis. [Pg.129]

Crescenzi, M., and Giuliani, A. 2001. The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data. FEES Lett 507 114—118. [Pg.147]

Wall, M. E., Rechtsteiner, A., and Rocha, L. M. 2003. Singular value decomposition and principal component analysis. In A Practical Approach to Microarray Data Analysis, (eds. D. R Berrar, W. Dubitzky, M. Granzow), pp. 91-109, Norwell, MA Kluwer. [Pg.148]