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Use of Principal Component Analysis and the GE ‐Biplot for the Graphical Exploration of Gene Expression Data
Author(s) -
Pittelkow Yvonne,
Wilson Susan R.
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00366.x
Subject(s) - biplot , principal component analysis , expression (computer science) , component (thermodynamics) , computational biology , gene expression , computer science , data mining , biology , gene , genetics , artificial intelligence , genotype , physics , programming language , thermodynamics
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.