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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...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2009
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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 |
format | Text |
id | pubmed-2638936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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