title: A random variance model for detection of differential gene expression in small microarray experiments.
abstract: BACKGROUND
  Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true.
  
  
  RESULTS
  We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives.
  
  
  BACKGROUND
  This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html).
  
  
  BACKGROUND
  ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf
authors.affiliation: National Cancer Institute Biometric Research Branch, National Institutes of Health, 6130 Executive Blvd., MSC 7434, Bethesda, MD 20892-7434, USA. wrightge@mail.nih.gov
authors.forename: George W
authors.initials: GW
authors.lastname: Wright
authors.forename: Richard M
authors.initials: RM
authors.lastname: Simon
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*