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Comp Funct Genomics. Apr 2005; 6(3): 116–122.
PMCID: PMC2447518
Meta-Analysis Combines Affymetrix Microarray Results Across Laboratories
John R. Stevens1 and R. W. Doergecorresponding author1,2
1 Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN, 47907-2067, USA,
2 Department of Agronomy, Lilly Hall of Life Sciences, 915 W. State Street, Purdue University, West Lafayette, IN, 47907-2054, USA,
R. W. Doerge, doerge/at/purdue.edu.
corresponding authorCorresponding author.
Received January 13, 2005; Accepted January 19, 2005.
Abstract
With microarray technology becoming more prevalent in recent years, it is now common for several laboratories to employ the same microarray technology to identify differentially expressed genes that are related to the same phenomenon in the same species. Although experimental specifics may be similar, each laboratory will typically produce a slightly different list of statistically significant genes, which calls into question the validity of each gene list (i.e. which list is best). A statistically-based meta-analytic approach to microarray analysis systematically combines results from the different laboratories to provide a single estimate of the degree of differential expression for each gene. This approach provides a more precise view of genes that are of significant interest, while simultaneously allowing for differences between laboratories. The widely-used Affymetrix oligonucleotide array and its software are of particular interest because the results 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 utility of such an approach in combining microarray results across laboratories.
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