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Statistical Applications in Genetics and Molecular Biology

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Volume 5, Issue 1 (Feb 2006)

Dimension Reduction for Classification with Gene Expression Microarray Data

Jian J Dai
  • University of California, Davis
/ Linh Lieu
  • University of California, Los Angeles
/ David Rocke
  • University of California, Davis
Published Online: 2006-02-24 | DOI: https://doi.org/10.2202/1544-6115.1147

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

About the article

Published Online: 2006-02-24

Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1147. Export Citation

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