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Briefings in Bioinformatics Advance Access originally published online on August 9, 2006
Briefings in Bioinformatics 2007 8(1):22-31; doi:10.1093/bib/bbl023
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© The Author 2006. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity

Jason C. Hsu, Jane Chang, Tao Wang, Eiríkur Steingrímsson, Magnús Karl Magnússon and Kristin Bergsteinsdottir

Corresponding author. Jason C. Hsu. Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Tel: 01 614 292 7663; Fax: 01 614 292 2096; E-mail: Hsu.1{at}osu.edu

Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarray studies in various cancers have reported such gene signatures. However, the overlap of gene signatures in the same disease has been limited so far, and some reported signatures have not been reproduced in other populations. Clearly, the methods used for verifying novel gene signatures need improvement. In this article, we describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication and blocking. Our results show that such designs provide unbiased estimation of differential expression levels as well as powerful tests for them.

Keywords: microarray experiments, experimental design, familywise error rate, multiple comparisons, sensitivity and specificity


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R. A. Verdugo, C. F. Deschepper, G. Munoz, D. Pomp, and G. A. Churchill
Importance of randomization in microarray experimental designs with Illumina platforms
Nucleic Acids Res., September 1, 2009; 37(17): 5610 - 5618.
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