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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/426

Title: Graphical exploration of gene expression data: a comparative study of three multivariate methods
Authors: Wouters, Luc
Gohlmann, Hinrich W.
Bijnens, Luc
Kass, Stefan U.
Molenberghs, Geert
Lewi, Paul J.
Keywords: Bioinformatics,Genetic data analysis
Multivariate data
Issue Date: 2003
Publisher: BLACKWELL PUBLISHING LTD
Citation: Biometrics, 59(4). p. 1131-1139
Abstract: This article describes three multivariate projection methods and compares them for their ability to identify clusters of biological samples and genes using real-life data on gene expression levels of leukemia patients. It is shown that principal component analysis (PCA) has the disadvantage that the resulting principal factors are not very informative, while correspondence factor analysis (CFA) has difficulties interpreting distances between objects. Spectral map analysis (SMA) is introduced as an alternative approach to the analysis of microarray data. Weighted SMA outperforms PCA, and is at least as powerful as CFA, in finding clusters in the samples, as well as identifying genes related to these clusters. SMA addresses the problem of data analysis in microarray experiments in a more appropriate manner than CFA, and allows more flexible weighting to the genes and samples. Proper weighting is important, since it enables less reliable data to be down-weighted and more reliable information to be emphasized.
URI: http://hdl.handle.net/1942/426
DOI: 10.1111/j.0006-341X.2003.00130.x
ISI #: 000187501100044
ISSN: 0006-341X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2005
Appears in Collections: Research publications

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