Journal Article

Power enhancement via multivariate outlier testing with gene expression arrays

Adam L. Asare, Zhong Gao, Vincent J. Carey, Richard Wang and Vicki Seyfert-Margolis

in Bioinformatics

Volume 25, issue 1, pages 48-53
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI:

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Motivation: As the use of microarrays in human studies continues to increase, stringent quality assurance is necessary to ensure accurate experimental interpretation. We present a formal approach for microarray quality assessment that is based on dimension reduction of established measures of signal and noise components of expression followed by parametric multivariate outlier testing.

Results: We applied our approach to several data resources. First, as a negative control, we found that the Affymetrix and Illumina contributions to MAQC data were free from outliers at a nominal outlier flagging rate of α=0.01. Second, we created a tunable framework for artificially corrupting intensity data from the Affymetrix Latin Square spike-in experiment to allow investigation of sensitivity and specificity of quality assurance (QA) criteria. Third, we applied the procedure to 507 Affymetrix microarray GeneChips processed with RNA from human peripheral blood samples. We show that exclusion of arrays by this approach substantially increases inferential power, or the ability to detect differential expression, in large clinical studies.

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Journal Article.  4061 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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