title: Sample size for gene expression microarray experiments.
abstract: BACKGROUND
  Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size.
  
  
  RESULTS
  We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays.
  
  
  BACKGROUND
  jchen@nctr.fda.gov.
authors.affiliation: Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
authors.forename: Chen-An
authors.initials: CA
authors.lastname: Tsai
authors.forename: Sue-Jane
authors.initials: SJ
authors.lastname: Wang
authors.forename: Dung-Tsa
authors.initials: DT
authors.lastname: Chen
authors.forename: James J
authors.initials: JJ
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA