Power enhancement via multivariate outlier testing with gene expression arrays. Export

@article{Asare2008, abstract = {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 alpha = 0:01. Second, we created a tunable framework for artificially corrupting intensity data from the Affymetrix Latin Square spikein experiment to allow investigation of sensitivity and specificity of 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: Subversion checkout at https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/arrayMvout, https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/affyContam (credentials: readonly/readonly), CONTACT: aasare@immunetolerance.org.}, author = {Asare, Adam and Gao, Zhong and Carey, Vincent J J. and Wang, Richard and Seyfert-Margolis, Vicki}, citeulike-article-id = {3811535}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btn591}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/1/48?etoc}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19015138}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19015138}, day = {16}, doi = {10.1093/bioinformatics/btn591}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {microarrays, model\_checking, r\_library}, month = {November}, posted-at = {2009-01-08 11:04:56}, priority = {2}, title = {Power enhancement via multivariate outlier testing with gene expression arrays.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn591}, year = {2008} }

TY - JOUR ID - Asare2008 L3 - citeulike-article-id:3811535 N2 - 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 alpha = 0:01. Second, we created a tunable framework for artificially corrupting intensity data from the Affymetrix Latin Square spikein experiment to allow investigation of sensitivity and specificity of 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: Subversion checkout at https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/arrayMvout, https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/affyContam (credentials: readonly/readonly), CONTACT: aasare@immunetolerance.org. JF - Bioinformatics (Oxford, England) SN - 1460-2059 TI - Power enhancement via multivariate outlier testing with gene expression arrays. KW - microarrays KW - model_checking KW - r_library AU - Asare, Adam AU - Gao, Zhong AU - Carey, Vincent J AU - Wang, Richard AU - Seyfert-Margolis, Vicki PY - 2008/11/16/ UR - http://dx.doi.org/10.1093/bioinformatics/btn591 ER -