Chapter 10. Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods

  1. Andreas Scherer Founder/CEO of Spheromics
  1. W Evan Johnson1 and
  2. Cheng Li2

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch10

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Johnson, W. E. and Li, C. (2009) Adjusting Batch Effects in Microarray Experiments with Small Sample Size Using Empirical Bayes Methods, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch10

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    Department of Statistics, Brigham Young University, Provo, UT, USA

  2. 2

    Department of Biostatistics, Dana-Farber Cancer Institute, Boston, MA, 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:

  • batch effect;
  • empirical Bayes;
  • Monte Carlo integration;
  • meta-analysis;
  • microarrays;
  • normalization

Summary

Nonbiological experimental variation or batch effects are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and nonparametric empirical Bayes frameworks for adjusting data for batch effects that are robust to outliers in small sample sizes and perform comparably to existing methods for large samples.