Premium
Graphical Exploration of Gene Expression Data: A Comparative Study of Three Multivariate Methods
Author(s) -
Wouters Luc,
Göhlmann Hinrich W.,
Bijnens Luc,
Kass Stefan U.,
Molenberghs Geert,
Lewi Paul J.
Publication year - 2003
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.0006-341x.2003.00130.x
Subject(s) - multivariate statistics , multivariate analysis , graphical model , expression (computer science) , computer science , computational biology , gene expression , statistics , gene , biology , mathematics , artificial intelligence , genetics , programming language
Summary . 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.