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The MicroArray Quality Control (MAQC) Project and Cross-Platform Analysis of Microarray Data

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

As a powerful tool for genome-wide gene expression analysis, DNA microarray technology is widely used in biomedical research. One important application of microarrays is to identify differentially expressed genes (DEGs) between two distinct biological conditions, e.g. disease versus normal or treatment versus control, so that the underlying molecular mechanism differentiating the two conditions maybe revealed. Mechanistic interpretation of microarray results requires the identification of reproducible and reliable lists of DEGs, because irreproducible lists of DEGs may lead to different biological conclusions. Many vendors are providing microarray platforms of different characteristics for gene expression analysis, and the widely publicized apparent lack of intra- and cross-platform concordance in DEGs from microarray analysis of the same sets of study samples has been of great concerns to the scientific community and regulatory agencies like the US Food and Drug Administration (FDA). In this chapter, we describe the study design of and the main findings from the FDA-led MicroArray Quality Control (MAQC) project that aims to objectively assess the performance of different microarray platforms and the advantages and limitations of various competing statistical methods in identifying DEGs from microarray data. Using large data sets generated on two human reference RNA samples established by the MAQC project, we show that the levels of concordance in inter-laboratory and cross-platform comparisons are generally high. Furthermore, the levels of concordance largely depend on the statistical criteria used for ranking and selecting DEGs, irrespective of the chosen platforms or test sites. Importantly, a straightforward method combining fold-change ranking with a non-stringent P-value cutoff produces more reproducible lists of DEGs than those by t-test P-value ranking. Similar conclusions are reached when microarray data sets from a rat toxicogenomics study are analyzed. The availability of the MAQC reference RNA samples and the large reference data sets provides a unique resource for the gene expression community to reach consensus on the “best practices” for the generation, analysis, and applications of microarray data in drug development and personalized medicine.

The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.

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Author information

Authors and Affiliations

  1. National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA

    Leming Shi

Authors
  1. Zhining Wen
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  2. Zhenqiang Su
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  3. Jie Liu
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  4. Baitang Ning
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  5. Lei Guo
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  6. Weida Tong
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  7. Leming Shi
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Corresponding author

Correspondence to Leming Shi .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Wen, Z. et al. (2011). The MicroArray Quality Control (MAQC) Project and Cross-Platform Analysis of Microarray Data. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_9

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