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Power enhancement via multivariate outlier testing with gene expression arrays

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...

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Detalles Bibliográficos
Autores principales: Asare, Adam L., Gao, Zhong, Carey, Vincent J., Wang, Richard, Seyfert-Margolis, Vicki
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2638936/
https://www.ncbi.nlm.nih.gov/pubmed/19015138
http://dx.doi.org/10.1093/bioinformatics/btn591
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author Asare, Adam L.
Gao, Zhong
Carey, Vincent J.
Wang, Richard
Seyfert-Margolis, Vicki
author_facet Asare, Adam L.
Gao, Zhong
Carey, Vincent J.
Wang, Richard
Seyfert-Margolis, Vicki
author_sort Asare, Adam L.
collection PubMed
description 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. Availability: http://bioconductor.org/packages/2.3/bioc/html/arrayMvout.html and http://bioconductor.org/packages/2.3/bioc/html/affyContam.html affyContam (credentials: readonly/readonly) Contact: aasare@immunetolerance.org; stvjc@channing.harvard.edu
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spelling pubmed-26389362009-02-25 Power enhancement via multivariate outlier testing with gene expression arrays Asare, Adam L. Gao, Zhong Carey, Vincent J. Wang, Richard Seyfert-Margolis, Vicki Bioinformatics Original Papers 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. Availability: http://bioconductor.org/packages/2.3/bioc/html/arrayMvout.html and http://bioconductor.org/packages/2.3/bioc/html/affyContam.html affyContam (credentials: readonly/readonly) Contact: aasare@immunetolerance.org; stvjc@channing.harvard.edu Oxford University Press 2009-01-01 2008-11-16 /pmc/articles/PMC2638936/ /pubmed/19015138 http://dx.doi.org/10.1093/bioinformatics/btn591 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Asare, Adam L.
Gao, Zhong
Carey, Vincent J.
Wang, Richard
Seyfert-Margolis, Vicki
Power enhancement via multivariate outlier testing with gene expression arrays
title Power enhancement via multivariate outlier testing with gene expression arrays
title_full Power enhancement via multivariate outlier testing with gene expression arrays
title_fullStr Power enhancement via multivariate outlier testing with gene expression arrays
title_full_unstemmed Power enhancement via multivariate outlier testing with gene expression arrays
title_short Power enhancement via multivariate outlier testing with gene expression arrays
title_sort power enhancement via multivariate outlier testing with gene expression arrays
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2638936/
https://www.ncbi.nlm.nih.gov/pubmed/19015138
http://dx.doi.org/10.1093/bioinformatics/btn591
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