Nucleic Acids Research Advance Access originally published online on May 1, 2008
Nucleic Acids Research 2008 36(10):e58; doi:10.1093/nar/gkn234
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Nucleic Acids Research, 2008, Vol. 36, No. 10 e58
© 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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Enhanced identification and biological validation of differential gene expression via Illumina whole-genome expression arrays through the use of the model-based background correction methodology
1Simmons Comprehensive Cancer Center Genomics Core Facility, 2Department of Radiation Oncology, Division of Molecular Radiation Biology, 3Simmons Comprehensive Cancer Center Biostatistics Core and 4Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA
*To whom correspondence should be addressed. Tel: +1 214 648 5557; Fax: +1 214 648 5995; Email: michael.story{at}utsouthwestern.edu
Received November 11, 2007. Revised April 11, 2008. Accepted April 14, 2008.
Despite the tremendous growth of microarray usage in scientific studies, there is a lack of standards for background correction methodologies, especially in single-color microarray platforms. Traditional background subtraction methods often generate negative signals and thus cause large amounts of data loss. Hence, some researchers prefer to avoid background corrections, which typically result in the underestimation of differential expression. Here, by utilizing nonspecific negative control features integrated into Illumina whole genome expression arrays, we have developed a method of model-based background correction for BeadArrays (MBCB). We compared the MBCB with a method adapted from the Affymetrix robust multi-array analysis algorithm and with no background subtraction, using a mouse acute myeloid leukemia (AML) dataset. We demonstrated that differential expression ratios obtained by using the MBCB had the best correlation with quantitative RT–PCR. MBCB also achieved better sensitivity in detecting differentially expressed genes with biological significance. For example, we demonstrated that the differential regulation of Tnfr2, Ikk and NF-kappaB, the death receptor pathway, in the AML samples, could only be detected by using data after MBCB implementation. We conclude that MBCB is a robust background correction method that will lead to more precise determination of gene expression and better biological interpretation of Illumina BeadArray data.
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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