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Biostatistics Advance Access published online on April 21, 2006
Biostatistics, doi:10.1093/biostatistics/kxj037
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.
Received February 28, 2006
Revised April 14, 2006
Accepted April 14, 2006

Article

Adjusting batch effects in microarray expression data using Empirical Bayes methods

W. Evan Johnson 1, Ariel Rabinovic 2, and Cheng Li 1 *

1 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA; Department of Biostatistics, Harvard School of Public Health, Boston, MA
2 Department of Genetics and Complex Diseases, Harvard School of Public Health, Boston, MA

* To whom correspondence should be addressed.
Cheng Li, E-mail: cli{at}hsph.harvard.edu


  Abstract

Non-biological 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 is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.


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