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

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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References

Ivanova N, Dimos J, Schaniel C, Hackney J, Moore K, Lemischka I . A stem cell molecular signature. Science. 2002; 298(5593):601-4. DOI: 10.1126/science.1073823. View

Ramalho-Santos M, Yoon S, Matsuzaki Y, Mulligan R, Melton D . "Stemness": transcriptional profiling of embryonic and adult stem cells. Science. 2002; 298(5593):597-600. DOI: 10.1126/science.1072530. View

Tusher V, Tibshirani R, Chu G . Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001; 98(9):5116-21. PMC: 33173. DOI: 10.1073/pnas.091062498. View

Guo L, Lobenhofer E, Wang C, Shippy R, Harris S, Zhang L . Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol. 2006; 24(9):1162-9. DOI: 10.1038/nbt1238. View

Patterson T, Lobenhofer E, Fulmer-Smentek S, Collins P, Chu T, Bao W . Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol. 2006; 24(9):1140-50. DOI: 10.1038/nbt1242. View

Ball C, Sherlock G, Parkinson H, Rocca-Sera P, Brooksbank C, Causton H . Standards for microarray data. Science. 2002; 298(5593):539. DOI: 10.1126/science.298.5593.539b. View

Tan P, Downey T, Spitznagel Jr E, Xu P, Fu D, Dimitrov D . Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 2003; 31(19):5676-84. PMC: 206463. DOI: 10.1093/nar/gkg763. View

Fortunel N, Otu H, Ng H, Chen J, Mu X, Chevassut T . Comment on " 'Stemness': transcriptional profiling of embryonic and adult stem cells" and "a stem cell molecular signature". Science. 2003; 302(5644):393. DOI: 10.1126/science.1086384. View

Tong W, Cao X, Harris S, Sun H, Fang H, Fuscoe J . ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research. Environ Health Perspect. 2003; 111(15):1819-26. PMC: 1241745. DOI: 10.1289/ehp.6497. View

10.

Tong W, Harris S, Cao X, Fang H, Shi L, Sun H . Development of public toxicogenomics software for microarray data management and analysis. Mutat Res. 2004; 549(1-2):241-53. DOI: 10.1016/j.mrfmmm.2003.12.024. View

11.

Gabor Miklos G, Maleszka R . Microarray reality checks in the context of a complex disease. Nat Biotechnol. 2004; 22(5):615-21. DOI: 10.1038/nbt965. View

12.

Mecham B, Klus G, Strovel J, Augustus M, Byrne D, Bozso P . Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucleic Acids Res. 2004; 32(9):e74. PMC: 419626. DOI: 10.1093/nar/gnh071. View

13.

Miller R, Callahan L, Casaceli C, Chen L, Kiser G, Chui B . Dysregulation of gene expression in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-lesioned mouse substantia nigra. J Neurosci. 2004; 24(34):7445-54. PMC: 6729635. DOI: 10.1523/JNEUROSCI.4204-03.2004. View

14.

Lesko L, Woodcock J . Translation of pharmacogenomics and pharmacogenetics: a regulatory perspective. Nat Rev Drug Discov. 2004; 3(9):763-9. DOI: 10.1038/nrd1499. View

15.

Marshall E . Getting the noise out of gene arrays. Science. 2004; 306(5696):630-1. DOI: 10.1126/science.306.5696.630. View

16.

Shi L, Tong W, Goodsaid F, Frueh F, Fang H, Han T . QA/QC: challenges and pitfalls facing the microarray community and regulatory agencies. Expert Rev Mol Diagn. 2004; 4(6):761-77. DOI: 10.1586/14737159.4.6.761. View

17.

Pruitt K, Tatusova T, Maglott D . NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 2004; 33(Database issue):D501-4. PMC: 539979. DOI: 10.1093/nar/gki025. View

18.

Dobbin K, Beer D, Meyerson M, Yeatman T, Gerald W, Jacobson J . Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin Cancer Res. 2005; 11(2 Pt 1):565-72. View

19.

Michiels S, Koscielny S, Hill C . Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005; 365(9458):488-92. DOI: 10.1016/S0140-6736(05)17866-0. View

20.

Larkin J, Frank B, Gavras H, Sultana R, Quackenbush J . Independence and reproducibility across microarray platforms. Nat Methods. 2005; 2(5):337-44. DOI: 10.1038/nmeth757. View