Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
6 Issues per year
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Rank 18 out of 117 in category Statistics & Probability in the 2012 Thomson Reuters Journal Citation Report/Science Edition
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Volume 12 (2013)
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Most Downloaded Articles
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- Normalization, bias correction, and peak calling for ChIP-seq by Diaz, Aaron/ Park, Kiyoub/ Lim, Daniel A. and Song, Jun S.
Dimension Reduction for Classification with Gene Expression Microarray Data
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, Pages –, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1147, February 2006
- 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.