Dimension Reduction for Classification with Gene Expression Microarray Data : Statistical Applications in Genetics and Molecular Biology

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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Dimension Reduction for Classification with Gene Expression Microarray Data

Jian J Dai1 / Linh Lieu2 / David Rocke3

1University of California, Davis

2University of California, Los Angeles

3University of California, Davis

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 5, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1147, February 2006

Publication History

Published Online:

An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) and principal component analysis (PCA), and evaluates the relative performance of classification procedures incorporating those methods. A five-step assessment procedure is designed for the purpose. Predictive accuracy and computational efficiency of the methods are examined. Two gene expression data sets for tumor classification are used in the study.

Keywords: partial least squares; sliced inverse regression; feature extraction; gene expression; tumor classification

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