Bioinformatics Advance Access originally published online on December 13, 2005
Bioinformatics 2006 22(4):472-476; doi:10.1093/bioinformatics/bti827
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Differential gene expression detection and sample classification using penalized linear regression models
Division of Biostatistics, School of Public Health, University of Minnesota A460 Mayo Building, MMC 303, Minneapolis, MN 55455, USA
Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p >> n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the ‘large p small n’ is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the ‘nearest shrunken centroid’ proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies.
Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the 
1 penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the 1 penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.
Availability: For the computer programs, detailed analysis results and R functions for the proposed methods, please see http://www.biostat.umn.edu/~baolin/research/L1C-mc.html
Contact: baolin{at}biostat.umn.edu
Supplementary information: http://www.biostat.umn.edu/~baolin/research/L1C-mc.html
Received on October 11, 2005; revised on December 6, 2005; accepted on December 7, 2005
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