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Microarray Data Analysis pp 139–152Cite as

Comparing Microarray Studies

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Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 377))

Abstract

We present a practical guide to some of the issues involved in comparing or integrating different microarray studies. We discuss the influence that various factors have on the agreement between studies, such as different technologies and platforms, statistical analysis criteria, protocols, and lab variability. We discuss methods to carry out or refine such comparisons, and detail several common pitfalls to avoid. Finally, we illustrate these ideas with an example case.

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References

  1. Barczak, A., Rodriguez, M. W., Hampers, K., et al. (2003) Spotted long oligonucleotide arrays for human gene expression analysis. Genome Res. 13, 1775–1785.

    Article  PubMed  CAS  Google Scholar 

  2. Kothapalli, R., Yoder, S. J., Mane, S., and Loughran, T. P., Jr. (2002) Microarray results: how accurate are they? BMC Bioinformatics 3, 22.

    Article  PubMed  Google Scholar 

  3. Kuo, W. P., Jenssen T. K., Butte A. J., et al. (2002) Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18, 405–412.

    Article  PubMed  CAS  Google Scholar 

  4. Mah, N., Thelin, A., Lu, T., et al. (2004) A comparison of oligonucleotide and cDNA-based microarray systems. Phys. Genom. 16, 361–370.

    Article  CAS  Google Scholar 

  5. Mecham, B. H., Klus, G. T., Strovel, J., et al. (2004) Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements. Nucl. Acids Res. 32, e74.

    Article  PubMed  Google Scholar 

  6. Rogojina, A., Orr, W. E., Song, B. K., and Geisert, E. E., Jr. (2003) Comparing the use of Affymetrix to spotted oligonucleotide microarrays using two retinal pigment epithelium cell lines. Mol. Vision 9, 482–496.

    CAS  Google Scholar 

  7. Tan, P., Downey, T. J., Spitznagel, E. L., Jr., et al. (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucl. Acids Res. 31, 5676–5684.

    Article  PubMed  CAS  Google Scholar 

  8. Yauk, C., Beendt, M. L, Williams, A., Douglas, G. R., et al. (2004) Comprehensive comparison of six microarray technologies. Nucl. Acids Res. 32, e124.

    Article  PubMed  Google Scholar 

  9. Yuen, T., Wurmbach, E., Pfeffer, R. L., Ebersole, B. J., and Sealfon, S. C. (2002) Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucl. Acids Res. 30, e48.

    Article  PubMed  Google Scholar 

  10. Irizarry, R. A., Warren, D., Spencer, F., et al. (2005) Multiple-laboratory comparison of microarray platforms (vol 2, pg 345, 2005). Nat. Methods 2, 477–477.

    Article  CAS  Google Scholar 

  11. Hekstra, D., Taussig, A. R., Magnasco, M., and Naef, F. (2003) Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays. Nucl. Acids Res. 31, 1962–1968.

    Article  PubMed  CAS  Google Scholar 

  12. Naef, F., Lim, D. A., Patil, N., and Magnasco, M. (2002) DNA hybridization to mismatched templates: A chip study. Phys. Rev. E. 65 (040902).

    Google Scholar 

  13. Naef, F and Magnasco, M. (2003) Solving the riddle of the bright mismatches: Labeling and effective binding in oligonucleotide arrays. Phys. Rev. E. 68 (011906).

    Google Scholar 

  14. Li, C. and Wong, W. (2001) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98, 31–36.

    Article  PubMed  CAS  Google Scholar 

  15. Irizarry, R. A., et al. (2004) Multiple Lab Comparisons of Microarray Platforms, in Dept. of Biostatistics Working Papers, Johns Hopkins University.

    Google Scholar 

  16. Irizarry, R. A., Bolstad, B. M., Collin, F., Cope, L. M., Hobbs, B., and Speed, T. P. (2003) Summaries of Affymetrix GeneChip probe level data. Nucl. Acids Res. 31, e15.

    Article  PubMed  Google Scholar 

  17. Yang, Y. H., Dudoit. S., Luu. P., et al. (2002) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucl. Acids Res. 30, e15.

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  19. Suárez-Fariñas, M., Haider, A., and Wittkowski, K. M. (2005) ‘Harshlighting’ small blemishes on microarrays. BMC Bioinformatics 6, 65.

    Article  PubMed  Google Scholar 

  20. Buness, A., Huber, W., Steiner, K., Suitmann, H., and Poustka, A. (2005) arrayMagic: two-colour cDNA microarray quality control and preprocessing. Bioinformatics 21, 554–556.

    Article  PubMed  CAS  Google Scholar 

  21. Parmigiani, G., Garrett-Mayer, E. S., Anbazhagan, R., and Gabrielson, E. (2004) A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clin. Cancer Res. 10, 2922–2927.

    Article  PubMed  CAS  Google Scholar 

  22. Culhane, A., Perriere, G., and Higgins, D. (2003) Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics 4, 1600–1608.

    Article  Google Scholar 

  23. Suárez-Fariñas, M., Noggle, S., Heke, M., Hemmati-Brivanlou, A. and Magnasco, M. O., et al. (2005) How to compare microarray studies: The case of human embryonic stem cells. BMC Genomics 4.

    Google Scholar 

  24. Rhodes, D., Barrette, T. R., Rubin, M. A., Ghosh, D., and Chinnaiyan, A. M. (2002) Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res. 62, 4427–4433.

    PubMed  CAS  Google Scholar 

  25. Bhattacharya, B., Miura, T., Brandenberger, R., et al. (2004) Gene expression in human embryonic stem cell lines: unique molecular signature. Blood 103, 2956–2964.

    Article  PubMed  CAS  Google Scholar 

  26. Sperger, J. M., Chen, X., Draper, J. S., et al. (2003) Gene expression patterns in human embryonic stem cells and human pluripotent germ cell tumors. Proc. Natl. Acad. Sci. USA 100, 13,350–13,355.

    Article  PubMed  CAS  Google Scholar 

  27. Sato, N., Sanjuan, I. M., Heke, M., et al. (2003) Molecular signature of human embryonic stem cells and its comparison with the mouse. Dev. Biol. 260, 404–413.

    Article  PubMed  CAS  Google Scholar 

  28. Available from: http://www.r-project.org. Last accessed: 10/19/2006.

  29. Available from: http://www.bioconductor.org. Last accessed: 10/19/2006.

  30. Yang, Y. H. and Thorne, N. (2003) Normalization for two-color cDNA microarray data. In: Science and Statistics: A Festschrift for Terry Speed, (Goldstein, D. R., ed.) IMS Lecture Notes Monograph Series, vol 40, pp. 403–418.

    Google Scholar 

  31. Wu, Z., Irizarry, R. A., Gentleman, R., Martinez-Murillo, F., and Spencer, F. (2004) A model based background adjustement for oligonucleotide expression arrays. J. Amer. Stat. Assoc. 99, 909–917.

    Article  Google Scholar 

  32. Lonnstedt, I. and Speed, T. (2002) Replicated microarray data. Statistica Sinica 12, 31–46.

    Google Scholar 

  33. Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article 3.

    Google Scholar 

  34. Dudoit, S., Shaffer, J., and Boldrick, J. (2003) Multiple hypothesis testing in microarray experiments. Stat. Sci. 18, 71–103.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Studies in Physics and Biology, The Rockefeller University, New York, NY

    Mayte Suárez-Fariñas & Marcelo O. Magnasco

Authors
  1. Mayte Suárez-Fariñas
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  2. Marcelo O. Magnasco
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada

    Michael J. Korenberg

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© 2007 Humana Press Inc., Totowa, NJ

About this protocol

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Suárez-Fariñas, M., Magnasco, M.O. (2007). Comparing Microarray Studies. In: Korenberg, M.J. (eds) Microarray Data Analysis. Methods in Molecular Biology™, vol 377. Humana Press. https://doi.org/10.1007/978-1-59745-390-5_8

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  • DOI: https://doi.org/10.1007/978-1-59745-390-5_8

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-540-8

  • Online ISBN: 978-1-59745-390-5

  • eBook Packages: Springer Protocols

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