Chapter 14. Visualization of Cross-Platform Microarray Normalization

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Xuxin Liu1,
  2. Joel Parker2,
  3. Cheng Fan,
  4. Charles M Perou2 and
  5. J S Marron3

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch14

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Liu, X., Parker, J., Fan, C., Perou, C. M. and Marron, J. S. (2009) Visualization of Cross-Platform Microarray Normalization, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch14

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    Department of Statistics, Harvard University, Cambridge, MA, USA

  2. 2

    Department of Genetics and Pathology, University of North Carolina, Chapel Hill, NC, USA

  3. 3

    Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, USA

Publication History

  1. Published Online: 2 NOV 2009
  2. Published Print: 30 OCT 2009

ISBN Information

Print ISBN: 9780470741382

Online ISBN: 9780470685983

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Keywords:

  • distance weighted discrimination (DWD);
  • cross-platform;
  • DiProPerm;
  • statistical power;
  • gene-by-gene;
  • multivariate

Summary

Combining different microarray data sets, even across platforms, is considered in this chapter. The larger sample sizes created in this way have the potential to generally increase statistical power. Distance weighted discrimination (DWD) has been shown to provide this improvement in some cases. We replicate earlier results indicating that DWD provides an effective approach to cross-platform batch adjustment, using both novel and conventional visualization methods. Improved statistical power from combining data is demonstrated for a new DWD based hypothesis test. This result appears to contradict a number of earlier results, which suggested that such data combination is not possible. The contradiction is resolved by understanding the differences between gene-by-gene analysis and our more complete and insightful multivariate approach of DWD.