title: An improved method for computing q-values when the distribution of effect sizes is asymmetric.
abstract: BACKGROUND
  Asymmetry is frequently observed in the empirical distribution of test statistics that results from the analysis of gene expression experiments. This asymmetry indicates an asymmetry in the distribution of effect sizes. A common method for identifying differentially expressed (DE) genes in a gene expression experiment while controlling false discovery rate (FDR) is Storey's q-value method. This method ranks genes based solely on the P-values from each gene in the experiment.
  
  
  RESULTS
  We propose a method that alters and improves upon the q-value method by taking the sign of the test statistics, in addition to the P-values, into account. Through two simulation studies (one involving independent normal data and one involving microarray data), we show that the proposed method, when compared with the traditional q-value method, generally provides a better ranking for genes as well as a higher number of truly DE genes declared to be DE, while still adequately controlling FDR. We illustrate the proposed method by analyzing two microarray datasets, one from an experiment of thale cress seedlings and the other from an experiment of maize leaves.
  
  
  METHODS
  The R code and data files for the proposed method and examples are available at Bioinformatics online.
authors.affiliation: Department of Statistics, North Dakota State University, Fargo, ND 58102, USA and Department of Statistics, Iowa State University, Ames, IA 50011, USA.
authors.forename: Megan
authors.initials: M
authors.lastname: Orr
authors.affiliation: Department of Statistics, North Dakota State University, Fargo, ND 58102, USA and Department of Statistics, Iowa State University, Ames, IA 50011, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
authors.affiliation: Department of Statistics, North Dakota State University, Fargo, ND 58102, USA and Department of Statistics, Iowa State University, Ames, IA 50011, USA.
authors.forename: Dan
authors.initials: D
authors.lastname: Nettleton
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D016008:Statistical Distributions; D003313:Zea mays / Q000235:genetics