Rejoinder to Use of Principal Component Analysis and the GE -Biplot
for the Graphical Exploration of Gene Expression Data |
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| Authors: | Luc Wouters Hinrich W Göhlmann Luc Bijnens Stefan U Kass Geert Molenberghs Paul J Lewi |
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| Institution: | Center for Statistics, Limburgs Universitair Centrum, Transnationale Universiteit Limburg, Universitaire Campus, Gebouw D, B-3590 Diepenbeek, Belgium;; Barrier Therapeutics nv, Cipalstraat 3, B-2440 Geel, Belgium;; Departments of Genomic Technologies;; Global Biometrics and Reporting;; Center for Molecular Design;Johnson & Johnson Pharmaceutical Research &Development, a division of Janssen Pharmaceutica NV, B2340 Beerse, Belgium |
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| Abstract: | Summary This note is in response to Wouters et al. (2003, Biometrics 59, 1131–1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE‐biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other. |
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| Keywords: | Bioinformatics Biplot Data visualization GE‐biplot Gene expression data Microarray data Multivariate exploratory data analysis Principal component analysis SVD |
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