The MicroArray Quality Control (MAQC) Project Shows Inter- and Intraplatform Reproducibility of Gene Expression Measurements

National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas 72079, USA.
Nature Biotechnology (Impact Factor: 41.51). 10/2006; 24(9):1151-61. DOI: 10.1038/nbt1239
Source: PubMed


Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

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Article: The MicroArray Quality Control (MAQC) Project Shows Inter- and Intraplatform Reproducibility of Gene Expression Measurements

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    • "Today, the most broadly used platforms are DNA microarrays and quantitative real-time PCR, although RNA sequencing is gaining popularity. Extensive work in this fi eld has demonstrated that although these platforms have varying degrees of sensitivity, the data produced from one platform are generally quite reproducible on another (e.g., Shi et al. 2006, Yauk and Berndt 2007). The type of data processing (i.e., background subtraction, fi ltering, and normalization ) applied for the analysis of gene expression data can impact the genes identifi ed as diff erentially expressed and the magnitude of the fold change (Steinhoff and Vingron 2006). "
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    • "Quality control was performed using log-ratio versus log-product (MA) plots and volcano plots (data not shown). Probes with a substantial likelihood of differential expression under treatment conditions were identified using simple t-tests combined with mean fold change in accordance with recommendations from the Microarray Quality Control Consortium (Guo et al. 2006; Shi et al. 2006). We selected a t-test p-value threshold of 0.1 and a minimum absolute fold difference of 2.0 between the controls and exposed data sets. "
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    • "MicroArray Quality Control Project (MAQC) data [31] includes biological samples for human brain reference RNA (hbr) and universal human reference RNA (uhr). Two different laboratories, Dr. Dudoit from University of California at Berkeley and Dr. Wu from Genentech, independently sequenced the samples using the Illumina GAII platform [32]. "
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