title: Combining Affymetrix microarray results.
abstract: BACKGROUND
  As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.
  
  
  RESULTS
  We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis.
  
  
  CONCLUSIONS
  The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest.
authors.affiliation: Department of Statistics, Purdue University, 150 N, University Street, West Lafayette, Indiana 47907-2067, USA. jrsteven@stat.purdue.edu
authors.forename: John R
authors.initials: JR
authors.lastname: Stevens
authors.forename: R W
authors.initials: RW
authors.lastname: Doerge
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D002874:Chromosome Mapping; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D003198:Computer Simulation; D018076:DNA, Complementary; D030541:Databases, Genetic; D004195:Disease Models, Animal; D004681:Encephalomyelitis, Autoimmune, Experimental / Q000235:genetics; D020869:Gene Expression Profiling; D016678:Genome; D006801:Humans; D015201:Meta-Analysis as Topic; D051379:Mice; D046228:Microarray Analysis; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D018076:DNA, Complementary