Visualization and analysis of molecular data.
This chapter provides an overview of visualization and analysis techniques applied to large-scale datasets from genomics, metabolomics, and proteomics. The aim is to reduce the number of variables (genes, metabolites, or proteins) by extracting a small set of new relevant variables, usually termed components. The advantages and disadvantages of the classical principal component analysis (PC A) are discussed and a link is given to the closely related singular value decomposition and multidimensional scaling. Special emphasis is given to the recent trend toward the use of independent component analysis, which aims to extract statistically independent components and, therefore, provides usually more meaningful components than PCA. We also discuss normalization techniques and their influence on the result of different analytical techniques.
Scholz
Matthias
M
Institute of Biochemistry and Biology, University of Potsdam, Germany.
Selbig
Joachim
J
D000465:Algorithms; D017360:Arabidopsis / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D013058:Mass Spectrometry / Q000379:methods; D025341:Principal Component Analysis / Q000379:methods*