title: Practical FDR-based sample size calculations in microarray experiments.
abstract: BACKGROUND
  Owing to the experimental cost and difficulty in obtaining biological materials, it is essential to consider appropriate sample sizes in microarray studies. With the growing use of the False Discovery Rate (FDR) in microarray analysis, an FDR-based sample size calculation is essential.
  
  
  METHODS
  We describe an approach to explicitly connect the sample size to the FDR and the number of differentially expressed genes to be detected. The method fits parametric models for degree of differential expression using the Expectation-Maximization algorithm.
  
  
  RESULTS
  The applicability of the method is illustrated with simulations and studies of a lung microarray dataset. We propose to use a small training set or published data from relevant biological settings to calculate the sample size of an experiment.
  
  
  BACKGROUND
  Code to implement the method in the statistical package R is available from the authors.
authors.affiliation: Department of Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center, TX 77030-4009, USA. jhu@mdanderson.org
authors.forename: Jianhua
authors.initials: J
authors.lastname: Hu
authors.forename: Fei
authors.initials: F
authors.lastname: Zou
authors.forename: Fred A
authors.initials: FA
authors.lastname: Wright
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D012984:Software