Abstract #301186

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JSM 2003 Abstract #301186
Activity Number: 250
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301186
Title: Linear Regression and Two-class Classification with Gene Expression Data
Author(s): Xiaohong Huang*+ and Wei Pan
Companies: University of Minnesota and University of Minnesota
Address: 1000 27th Ave. SE-Apt. E, Minneapolis, MN, 55414-2752,
Keywords: weighted voting ; compound covariates ; shrunken centroids ; partial least square ; penalized regression ; discriminant analysis
Abstract:

Using gene expression data to classify (or predict) tumor types has received much research attention recently. Due to special features of gene expression data, several new methods have been proposed, including the weighted voting scheme of Golub et al. (1999), the compound covariate method of Hedenfalk et al. (2001) (originally proposed by Tukey 1993), and the shrunken centroids method of Tibshirani et al. (2002). These methods look different and are more or less ad hoc. Here we point out a close connection of the three methods with a linear regression model. Casting the classification problem in the general framework of linear regression naturally leads to new alternatives, such as modified partial least squares (PLS) methods and penalized PLS (PPLS) methods. Using two real datasets, we show the competitive performance of our new methods when compared with the other three methods.

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