title: A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.
abstract: BACKGROUND
  Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.
  
  
  RESULTS
  Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.
  
  
  CONCLUSIONS
  Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better.
  
  
  BACKGROUND
  The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.
authors.affiliation: Unité de Recherche en Biologie Moléculaire, Facultés Universitaires Notre-Dame de la Paix (F.U.N.D.P.), Rue de Bruxelles 61, B-5000 Namur, Belgium.
authors.forename: Benoît
authors.initials: B
authors.lastname: De Hertogh
authors.forename: Bertrand
authors.initials: B
authors.lastname: De Meulder
authors.forename: Fabrice
authors.initials: F
authors.lastname: Berger
authors.forename: Michael
authors.initials: M
authors.lastname: Pierre
authors.forename: Eric
authors.initials: E
authors.lastname: Bareke
authors.forename: Anthoula
authors.initials: A
authors.lastname: Gaigneaux
authors.forename: Eric
authors.initials: E
authors.lastname: Depiereux
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*