Nucleic Acids Research Advance Access originally published online on January 4, 2008
Nucleic Acids Research 2008 36(2):e11; doi:10.1093/nar/gkm1075
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Nucleic Acids Research, 2008, Vol. 36, No. 2 e11
© 2008 The Author(s)
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.
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Model-based variance-stabilizing transformation for Illumina microarray data
1Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, 60611, USA and 2European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge CB10 1SD, UK
*To whom correspondence should be addressed. Tel: 1 312 695 1331; Fax: 1 312 695 1352; Email: s-lin2{at}northwestern.edu
Received May 3, 2007. Revised November 14, 2007. Accepted November 15, 2007.
Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
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