Optimal designs for 2-color microarray experiments.
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.
Sanchez
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PS
Discipline of Statistics, School of Mathematical Sciences, The University of Adelaide, SA 5005, Australia. penny.sanchez@adelaide.edu.au
Glonek
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GF
D001699:Biometry; D001943:Breast Neoplasms / Q000201:enzymology / Q000235:genetics; D045744:Cell Line, Tumor; D003116:Color; D005260:Female; D005456:Fluorescent Dyes; D006801:Humans; D007938:Leukemia / Q000235:genetics; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D017853:Phosphotransferases (Alcohol Group Acceptor) / Q000235:genetics; D000099157:Sphingosine Kinase
D005456:Fluorescent Dyes; D017853:Phosphotransferases (Alcohol Group Acceptor); D000099157:Sphingosine Kinase