Use of principal component analysis and the GE-biplot for the graphical exploration of gene expression data.
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.
Pittelkow
Yvonne
Y
Centre for Bioinformation Science, Mathematical Sciences Institute, Australian National University, Canberra, ACT 0200, Australia.
Wilson
Susan R
SR
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D015870:Gene Expression; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results