title: Effects of pooling mRNA in microarray class comparisons.
abstract: BACKGROUND
  In microarray experiments investigators sometimes wish to pool RNA samples before labeling and hybridization due to insufficient RNA from each individual sample or to reduce the number of arrays for the purpose of saving cost. The basic assumption of pooling is that the expression of an mRNA molecule in the pool is close to the average expression from individual samples. Recently, a method for studying the effect of pooling mRNA on statistical power in detecting differentially expressed genes between classes has been proposed, but the different sources of variation arising in microarray experiments were not distinguished. Another paper recently did take different sources of variation into account, but did not address power and sample size for class comparison. In this paper, we study the implication of pooling in detecting differential gene expression taking into account different sources of variation and check the basic assumption of pooling using data from both the cDNA and Affymetrix GeneChip microarray experiments.
  
  
  RESULTS
  We present formulas for the required number of subjects and arrays to achieve a desired power at a specified significance level. We show that due to the loss of degrees of freedom for a pooled design, a large increase in the number of subjects may be required to achieve a power comparable to that of a non-pooled design. The added expense of additional samples for the pooled design may outweigh the benefit of saving on microarray cost. The microarray data from both platforms show that the major assumption of pooling may not hold.
  
  
  BACKGROUND
  Supplementary material referenced in the text is available at http://linus.nci.nih.gov/brb/TechReport.htm.
authors.affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD 20892, USA. jshih@mail.nih.gov <jshih@mail.nih.gov>
authors.forename: Joanna H
authors.initials: JH
authors.lastname: Shih
authors.forename: Aleksandra M
authors.initials: AM
authors.lastname: Michalowska
authors.forename: Kevin
authors.initials: K
authors.lastname: Dobbin
authors.forename: Yumei
authors.initials: Y
authors.lastname: Ye
authors.forename: Ting Hu
authors.initials: TH
authors.lastname: Qiu
authors.forename: Jeffrey E
authors.initials: JE
authors.lastname: Green
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000235:genetics*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
chemical_list: D012333:RNA, Messenger