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Performance comparison of one-color and two-color platforms within the Microarray Quality Control (MAQC) project

Nature Biotechnology volume 24, pages 1140–1150 (2006)Cite this article

Abstract

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design.

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Figure 1: Volcano plots depicting estimated fold change (log2, x-axis) and statistical significance (−log10 P value, y-axis).
Figure 2: Comparison of log2 fold-change estimate results from three different modeling approaches for the three different platforms.
Figure 3: Comparison of negative log10 P-value estimate results from three different modeling approaches for the three different platforms.
Figure 4: Comparison of Agilent one-color and two-color data with TaqMan assay data.

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References

  1. Fodor, S.P. et al. Light-directed, spatially addressable parallel chemical synthesis. Science 251, 767–773 (1991).

    Article  CAS  Google Scholar 

  2. Fodor, S.P. et al. Multiplexed biochemical assays with biological chips. Nature 364, 555–556 (1993).

    Article  CAS  Google Scholar 

  3. Schena, M. et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    Article  CAS  Google Scholar 

  4. Churchill, G.A. Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32, Suppl. 490–494 (2002).

    Article  CAS  Google Scholar 

  5. Li, J., Pankratz, M. & Johnson, J. Differential gene expression patterns revealed by oligonucleotide versus long cDNA arrays. Toxicol. Sci. 69, 383–390 (2002).

    Article  CAS  Google Scholar 

  6. Tan, P. et al. Evaluation of gene expression measurements from commercial platforms. Nucleic Acids Res. 31, 5676–5684 (2003).

    Article  CAS  Google Scholar 

  7. Dobbin, K.K. et al. Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin. Cancer Res. 11, 565–572 (2005).

    CAS  PubMed  Google Scholar 

  8. Irizarry, R.A. et al. Multiple-laboratory comparison of microarray platforms. Nat. Methods 2, 345–349 (2005).

    Article  CAS  Google Scholar 

  9. Larkin, J.E., Frank, B.C., Gavras, H., Sultana, R. & Quackenbush, J. Independence and reproducibility across microarray platforms. Nat. Methods 2, 337–343 (2005).

    Article  CAS  Google Scholar 

  10. Kuo, W.P. et al. A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies. Nat. Biotechnol. 24, 832–840 (2006).

    Article  CAS  Google Scholar 

  11. Järvinen, A-K. et al. Are data from different gene expression microarray platforms comparable? Genomics 83, 1164–1168 (2004).

    Article  Google Scholar 

  12. de Reynies, A. et al. Comparison of the latest commercial short and long oligonucleotide microarray technologies. BMC Genomics 7, 51 (2006).

    Article  Google Scholar 

  13. Wang, Y. et al. Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays. BMC Genomics 7, 59 (2006).

    Article  Google Scholar 

  14. Bammler, T. et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat. Methods 2, 351–356 (2005).

    Article  Google Scholar 

  15. MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intramolecular reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).

  16. Canales, R.D. et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24, 1115–1122 (2006).

    Article  CAS  Google Scholar 

  17. Guo, Y. et al. Genomic analysis of anti-hepatitis B virus (HBV) activity by small interfering RNA and lamivudine in stable HBV-producing cells. J. Virol. 79, 14392–14403 (2005).

    Article  CAS  Google Scholar 

  18. Barczak, A. Spotted long oligonucleotide arrays for human gene expression analysis. Genome Res. 13, 1175–1785 (2003).

    Article  Google Scholar 

  19. Shi, L. et al. Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics 6 Suppl. 2, S12 (2005).

    Article  Google Scholar 

  20. Tong, W. et al. Development of public toxicogenomics software for microarray data management and analysis. Mutat. Res. 549, 241–253 (2004).

    Article  CAS  Google Scholar 

  21. Wolfinger, R.D. et al. Assessing gene significance from cDNA microarray data via mixed models. J. Comput. Biol. 8, 625–637 (2001).

    Article  CAS  Google Scholar 

  22. Jin, W., Riley, R., Wolfinger, R.D., White, K.P, Passador-Gurgel, G. & Gibson G. Contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat. Genet. 29, 389–395 (2001).

    Article  CAS  Google Scholar 

  23. Chu, T-M., Deng, S., Wolfinger, R.D., Paules, R.S. & Hamadeh, H.K. Cross-site comparison of gene expression data reveals high similarity. Environ. Health Perspect. 112, 449–455 (2004).

    Article  CAS  Google Scholar 

  24. Chu, T-M., Deng, S. & Wolfinger, R.D. Modeling Affymetrix data at the probe level. in DNA microarray and statistical genomics techniques: Design, analysis, and interpretation of experiment. (eds. Edwards, J.W., Beasley, T.M., Page, G.P. and Allison, D.B.) 197–222 (Chapman & Hall/CRC, Taylor & Francis Group, Boca Raton, FL, 2006).

    Google Scholar 

Download references

Acknowledgements

The authors thank the MicroArray Quality Control (MAQC) consortium for generating the large data sets used in this study. E.K.L. and P.H. acknowledge the Advanced Technology Program of the National Institute of Standards and Technology, whose generous support provided partial funding of this research (70NANB2H3009).

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Authors and Affiliations

  1. National Center for Toxicological Research, US Food & Drug Administration, 3900 NCTR Rd., Jefferson, 72079, Arkansas, USA

    Tucker A Patterson, James C Fuscoe, Weida Tong & Leming Shi

  2. A Division of Clinical Data, Cogenics, 100 Perimeter Park Drive, Suite C, Morrisville, 27560, North Carolina, USA

    Edward K Lobenhofer & Patrick Hurban

  3. Integrated Biology Solutions, Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, 95052-8059, California, USA

    Stephanie B Fulmer-Smentek & Patrick J Collins

  4. SAS Institute Inc., SAS Campus Drive, Cary, 27513, North Carolina, USA

    Tzu-Ming Chu, Wenjun Bao & Russell D Wolfinger

  5. Division of Bioinformatics, Z-Tech Corporation at NCTR/FDA, 3900 NCTR Rd., Jefferson, 72079, Arkansas, USA

    Hong Fang

  6. NCI Advanced Technology Center, 8717 Grovemont Circle, Bethesda, 20892-4605, Maryland, USA

    Ernest S Kawasaki

  7. Yale University, W.M. Keck Biotechnology Resource Laboratory, Microarray Resource, 300 George St., Suite 2110, New Haven, 06511, Connecticut, USA

    Janet Hager & Irina R Tikhonova

  8. Department of Physiology & Pharmacology, Wake Forest University School of Medicine, 115 S. Chestnut St., Winston-Salem, 27101, North Carolina, USA

    Stephen J Walker

  9. CapitalBio Corporation, 18 Life Science Parkway, Changping District, Beijing, 102206, P.R., China

    Liang Zhang

  10. Gene Expression Chips, Eppendorf Array Technologies (EAT), 20, rue du seminaire, Namur, 5000, Belgium

    Francoise de Longueville

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  1. Tucker A Patterson
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Corresponding author

Correspondence to Tucker A Patterson.

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Competing interests

S.B.F.-S., P.J.C. and L.Z. declare competing financial interests. While engaged in this research project, they were employed by two separate companies that manufacture microarrays. These two companies provided two of the three platforms used in this study.

Supplementary information

Supplementary Fig. 1

Example scatter plot matrices from each platform (PDF 114 kb)

Supplementary Fig. 2

Comparison of B/A ratios for Agilent one-color and two-color designs (Site 1) (PDF 115 kb)

Supplementary Fig. 3

Comparison of the normalization procedures on two-color TeleChem data: Lowess_Medium Scaling (LO_MS) versus Medium Scaling_Lowess (MS_LO) (PDF 19 kb)

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Patterson, T., Lobenhofer, E., Fulmer-Smentek, S. et al. Performance comparison of one-color and two-color platforms within the Microarray Quality Control (MAQC) project. Nat Biotechnol 24, 1140–1150 (2006). https://doi.org/10.1038/nbt1242

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