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Open Access Highly Accessed Methodology article

Assessing affymetrix GeneChip microarray quality

Matthew N McCall1, Peter N Murakami2, Margus Lukk34, Wolfgang Huber5 and Rafael A Irizarry6*

Author Affiliations

1 Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Ave., Rochester, NY, USA

2 Center for Epigenetics, Johns Hopkins School of Medicine, 855 N. Wolfe St., Baltimore, MD, USA

3 EMBL-EBI Functional Genomics Group, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

4 Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 ORE, UK

5 EMBL Genome Biology Unit, 69117 Heidelberg, Germany

6 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, USA

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BMC Bioinformatics 2011, 12:137  doi:10.1186/1471-2105-12-137

Published: 7 May 2011

Abstract

Background

Microarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality.

Results

We begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies.

Conclusions

We find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.