• A Corrigendum to this article was published on 01 June 2005


Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC–Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

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We thank A. Nones and K. Broman for useful suggestions. The work of R.A.I. is partially funded by the National Institutes of Health Specialized Centers of Clinically Oriented Research (SCCOR) translational research funds (212-2494 and 212-2496). The work of G. Germino and I. Kim was partially funded by NIDDK U24DK58757.

Author information

Author notes

    • Michael Wilson

    Present address: Ambion, Inc., Austin, Texas, 78744, USA


  1. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, 21205, Maryland, USA

    • Rafael A Irizarry
  2. Department of Surgery, Johns Hopkins University, Baltimore, 21205, Maryland, USA

    • Daniel Warren
    •  & Yanqin Yang
  3. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    • Forrest Spencer
  4. Department of Medicine, JHU NIDDK Gene Profiling Center, Johns Hopkins University, Baltimore, 21205, Maryland, USA

    • Irene F Kim
    •  & Gregory Germino
  5. Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, 21205, Maryland, USA

    • Shyam Biswal
    •  & Hannah Lee
  6. The Institute for Genomic Research, 9712 Medical Center Dr., Rockville, 20878, Maryland, USA

    • Bryan C Frank
    •  & John Quackenbush
  7. Department of Pathology, Johns Hopkins University, Baltimore, 21231, Maryland, USA

    • Edward Gabrielson
  8. Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Mason F. Lord Bldg., Center Tower #665, Baltimore, 21224, Maryland, USA

    • Joe G N Garcia
    •  & Shui Qing Ye
  9. NCI's Microarray Core Facility, Advanced Technology Center, Gaithersburg, 20877, Maryland, USA

    • Joel Geoghegan
    • , Ernest Kawasaki
    •  & David Petersen
  10. Department of Pathology, Johns Hopkins University, School of Medicine, 21287, Maryland, Baltimore, USA

    • Constance Griffin
    •  & Laura Morsberger
  11. Research Center for Genetic Medicine, Children's National Medical Center, George Washington University, Washington, 20052, DC, USA

    • Sara C Hilmer
    •  & Eric Hoffman
  12. W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, 21205, Maryland, USA

    • Anne E Jedlicka
    •  & Alan Scott
  13. Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, 21205, Maryland, USA

    • Francisco Martínez-Murillo
  14. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, 02115-6084, Massachusetts, USA

    • John Quackenbush
  15. Microarray Research Facility, Research Technologies Branch, DIR, National Institute of Allergy and Infectious Diseases, Bethesda, 20892, Maryland, USA

    • Michael Wilson
  16. Oncology Microarray Facility, Johns Hopkins University, Baltimore, 21231, Maryland, USA

    • Wayne Yu


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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Rafael A Irizarry.

Supplementary information

  1. Supplementary Fig. 1

    Observed log fold change versus RT-PCR log fold change for the four altered genes and 12 selected genes. (PDF 672 kb)

  2. Supplementary Fig. 2

    Preprocessing effect. (PDF 1279 kb)

  3. Supplementary Fig. 3

    SD (measure of precision) plotted against experience of technician for each of the five Affymetrix labs. (PDF 453 kb)

  4. Supplementary Fig. 4

    Precision assessment for the new Affymetrix chip. (PDF 525 kb)

  5. Supplementary Table 1

    Within and across lab assessment measure comparing preprocessing procedures as for Supplementary Figure 2. (PDF 52 kb)

  6. Supplementary Table 2

    Effect of annotation on across-lab agreement. (PDF 59 kb)

  7. Supplementary Table 3

    Across-lab agreement. (PDF 61 kb)

  8. Supplementary Table 4

    Assessments for results from lab 5 using old and new chip compared to results from the best performing labs. (PDF 48 kb)

  9. Supplementary Methods (PDF 67 kb)

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