Department of Public Health Sciences
|Dimension reduction for classification with gene expression microarray data
Dai JJ, Lieu L, Rocke D
Statistical Applications in Genetics and Molecular Biology. 2006. 5:Article 6.
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.
Reader's reaction to "Dimension reduction for classification with gene expression microarray data" by Dai et al (2006). [Stat Appl Genet Mol Biol. 2006]
[PubMed - indexed for MEDLINE]
Keywords: partial least squares; sliced inverse regression; feature extraction; gene expression; tumor classification
UC Davis Health System is pleased to provide this information for
general reference purposes only. It should not be considered as
a substitute for professional medical advice. You are urged to consult
with your health care provider for diagnosis of and treatment for
any health-related condition. The information provided herein may
not and should not be used for diagnosis and treatment.
Reproduction of material on this web site is hereby granted solely
for personal use. No other use of this material is authorized without
prior written approval of UC Regents.