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Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity.

Jason C Hsu·Jane Chang·Tao Wang·Eiríkur Steingrímsson·Magnús Karl Magnússon·Kristin Bergsteinsdottir
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Abstract

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.

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Breast NeoplasmsFemaleGene Expression ProfilingHumansMiddle AgedModels, StatisticalOligonucleotide Array Sequence AnalysisRandom AllocationReproducibility of ResultsResearch DesignSensitivity and SpecificityJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.

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Jason C Hsu, Jane Chang, Tao Wang, Eiríkur Steingrímsson, Magnús Karl Magnússon, Kristin Bergsteinsdottir. 2006-08-09. Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity.. https://doi.org/10.1093/bib%2Fbbl023

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