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Gene selection for microarray data analysis using principal component analysis
Author(s) -
Wang Antai,
Gehan Edmund A.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2082
Subject(s) - principal component analysis , automatic summarization , microarray analysis techniques , computer science , data set , selection (genetic algorithm) , data mining , gene selection , set (abstract data type) , curse of dimensionality , microarray databases , computational biology , artificial intelligence , gene , gene expression , biology , genetics , programming language
Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure. Copyright © 2005 John Wiley & Sons, Ltd.