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Bioinformatics Advance Access published online on December 14, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti217
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received November 9, 2004
Revised December 8, 2004
Accepted December 9, 2004

Article

Differential gene expression detection using penalized linear regression models: the improved SAM statistics

Baolin Wu 1*

1 Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN, 55455, USA

* To whom correspondence should be addressed.
Baolin Wu, E-mail: baolin{at}biostat.umn.edu


  Abstract

Differential gene expression detection using microarrays has received lots research interests recently. Lots of methods have been proposed, including variants of F-statistics (Golub et al. (1999), Dudoit et al. (2002)), nonparametric approaches (Pan (2003)), and empirical Bayesian methods of Efron et al. (2001) etc. For a comparative review please see Pan (2002). The SAM statistics proposed by Tusher et al. (2001) has been shown to have good performance in empirical studies. SAM is more like an ad-hoc shrinkage method. The idea is that for small sample microarray data, it is often useful to pool information across genes to improve efficiency. Under Bayesian framework Smyth (2004) formally derived the test statistics with shrinkage using the hierarchical models. In this paper we cast differential gene expression detection in the familiar framework of linear regression model. Commonly used test statistics correspond to using least squares to estimate the regression parameters. Based on the vast literature of research on linear models, we can naturally consider other alternatives. Here we explore the penalized linear regression. We propose the penalized t/F-statistics for two-class microarray data based on L1 penalty. We will show that the penalized test statistics intuitively makes sense and through applications we illustrate its good performance. Supplementary information including program codes, more detailed analysis results and R functions for the proposed methods can be found at http://www.biostat.umn.edu/~baolin/research.


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