title: Differential gene expression detection using penalized linear regression models: the improved SAM statistics.
abstract: 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 [Formula: see text] penalty. We will show that the penalized test statistics intuitively makes sense and through applications we illustrate its good performance.
  
  
  BACKGROUND
  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
  
  
  BACKGROUND
  baolin@biostat.umn.edu
  
  
  BACKGROUND
  http://www.biostat.umn.edu/~baolin/research.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455, USA. baolin@biostat.umn.edu
authors.forename: Baolin
authors.initials: B
authors.lastname: Wu
mesh_terms: D045744:Cell Line, Tumor; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000528:radiation effects*; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011839:Radiation, Ionizing; D012044:Regression Analysis; D012984:Software
chemical_list: D009363:Neoplasm Proteins