Biostatistics Advance Access originally published online on April 28, 2009
Biostatistics 2009 10(3):561-574; doi:10.1093/biostatistics/kxp012
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Optimal designs for 2-color microarray experiments
Discipline of Statistics, School of Mathematical Sciences, The University of Adelaide, SA 5005, Australia
penny.sanchez{at}adelaide.edu.au
* To whom correspondence should be addressed.
Statisticians can play a crucial role in the design of gene expression studies to ensure the most effective allocation of available resources. This paper considers Pareto optimal designs for gene expression studies involving 2-color microarrays. Pareto optimality enables the recommendation of designs that are particularly efficient for the effects of most interest to biologists. This is relevant in the microarray context where analysis is typically carried out separately for those effects. Our approach will allow for effects of interest that correspond to contrasts rather than solely considering parameters of the linear model. We further develop the approach to cater for additional experimental considerations such as contrasts that are of equal scientific interest. This amounts to partitioning all relevant contrasts into subsets of effects that are of equal importance. Based on the partitions, a penalty is employed in order to recommend designs for complex and varied microarray experiments. Finally, we address the issue of gene-specific dye bias. We illustrate using studies of leukemia and breast cancer.
Keywords: Factorial experiments; Microarrays; Optimal experimental design
Received April 30, 2007; revised November 7, 2007; revised May 16, 2008; revised October 3, 2008; revised November 17, 2008; revised February 4, 2009; accepted for publication March 24, 2009.