Chapter 13. Batch Profile Estimation, Correction, and Scoring

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Tzu-Ming Chu1,
  2. Wenjun Bao1,
  3. Russell S Thomas2 and
  4. Russell D Wolfinger1

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch13

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Chu, T.-M., Bao, W., Thomas, R. S. and Wolfinger, R. D. (2009) Batch Profile Estimation, Correction, and Scoring, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch13

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    SAS Institute Inc., Cary, NC, USA

  2. 2

    Hamner Institutes for Health Services, Research Triangle Park, 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:

  • batch correction;
  • batch profile;
  • batch scoring;
  • grouped-batch-profile (GBP) normalization;
  • cross-validation

Summary

Batch effects increase the variation among expression data and, hence, reduce the statistical power for investigating biological effects. When the proportion of variation associated with the batch effect is high, it is desirable to remove the batch effect from data. A robust batch effect removal method should be easily applicable to new batches. This chapter discusses a simple but robust grouped-batch-profile (GBP) normalization method that includes three steps: batch profile estimation, correction, and scoring. Genes with similar expression patterns across batches are grouped. The method assumes the availability of control samples in each batch, and the corresponding batch profile of each group is estimated by analysis of variance. Batch correction and scoring are based on the estimated profiles. A mouse lung tumorigenicity data set is used to illustrate GBP normalization through cross-validation on 84 predictive models. On average, cross-validated predictive accuracy increases significantly after GBP normalization.