Nucleic Acids Research Advance Access originally published online on May 3, 2007
Nucleic Acids Research 2007 35(10):e72; doi:10.1093/nar/gkl1133
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Nucleic Acids Research, 2007, Vol. 35, No. 10 e72
© 2007 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.
Methods Online |
Three methods for optimization of cross-laboratory and cross-platform microarray expression data
1Biodesign Institute, Arizona State University, Center for Innovations in Medicine, Tempe, AZ, USA and 2Translational Genomics Research Institute, Computational Biology Division, Phoenix, AZ, USA
Received April 21, 2006. Revised December 8, 2006. Accepted December 13, 2006.
Microarray gene expression data becomes more valuable as our confidence in the results grows. Guaranteeing data quality becomes increasingly important as microarrays are being used to diagnose and treat patients (1–4). The MAQC Quality Control Consortium, the FDA's Critical Path Initiative, NCI's caBIG and others are implementing procedures that will broadly enhance data quality. As GEO continues to grow, its usefulness is constrained by the level of correlation across experiments and general applicability. Although RNA preparation and array platform play important roles in data accuracy, pre-processing is a user-selected factor that has an enormous effect. Normalization of expression data is necessary, but the methods have specific and pronounced effects on precision, accuracy and historical correlation. As a case study, we present a microarray calibration process using normalization as the adjustable parameter. We examine the impact of eight normalizations across both Agilent and Affymetrix expression platforms on three expression readouts: (1) sensitivity and power, (2) functional/biological interpretation and (3) feature selection and classification error. The reader is encouraged to measure their own discordant data, whether cross-laboratory, cross-platform or across any other variance source, and to use their results to tune the adjustable parameters of their laboratory to ensure increased correlation.
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