title: Statistical implications of pooling RNA samples for microarray experiments.
abstract: BACKGROUND
  Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated.
  
  
  RESULTS
  Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using "virtual" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost.
  
  
  CONCLUSIONS
  Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted.
authors.affiliation: Department of Statistics, University of Kentucky, Lexington, KY 40506, USA. peng@ms.uky.edu
authors.forename: Xuejun
authors.initials: X
authors.lastname: Peng
authors.forename: Constance L
authors.initials: CL
authors.lastname: Wood
authors.forename: Eric M
authors.initials: EM
authors.lastname: Blalock
authors.forename: Kuey Chu
authors.initials: KC
authors.lastname: Chen
authors.forename: Philip W
authors.initials: PW
authors.lastname: Landfield
authors.forename: Arnold J
authors.initials: AJ
authors.lastname: Stromberg
mesh_terms: D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation / Q000706:statistics & numerical data; D036262:Empirical Research; D020869:Gene Expression Profiling / Q000191:economics / Q000379:methods / Q000706:statistics & numerical data*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data*; D010865:Pilot Projects; D012313:RNA / Q000032:analysis* / Q000235:genetics*; D012107:Research Design / Q000706:statistics & numerical data; D018401:Sample Size
chemical_list: D012313:RNA