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


BACKGROUND
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)
Asare
Adam L
AL
Immune Tolerance Network, University of California - San Francisco, San Francisco, CA 94143, USA. aasare@immunetolerance.org
Gao
Zhong
Z
Carey
Vincent J
VJ
Wang
Richard
R
Seyfert-Margolis
Vicki
V
D031164:Ambrosia / Q000276:immunology; D002986:Clinical Trials as Topic; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D006967:Hypersensitivity / Q000235:genetics / Q000276:immunology; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis