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

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Summary: Differential gene expression detection using microarrays has received lots of research interests recently. Many methods have been proposed, including variants of F-statistics, non-parametric approaches and empirical Bayesian methods etc. The SAM statistics 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 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.

Original languageEnglish (US)
Pages (from-to)1565-1571
Number of pages7
JournalBioinformatics
Volume21
Issue number8
DOIs
StatePublished - Apr 15 2005

Bibliographical note

Funding Information:
This research was supported by a startup fund from the Division of Biostatistics, University of Minnesota. The author would like to

Fingerprint

Dive into the research topics of 'Differential gene expression detection using penalized linear regression models: The improved SAM statistics'. Together they form a unique fingerprint.

Cite this