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The practical effect of batch on genomic prediction

Author

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  • Parker Hilary S.

    (Johns Hopkins Bloomberg School of Public Health)

  • Leek Jeffrey T.

    (Johns Hopkins Bloomberg School of Public Health)

Abstract

Measurements from microarrays and other high-throughput technologies are susceptible to non-biological artifacts like batch effects. It is known that batch effects can alter or obscure the set of significant results and biological conclusions in high-throughput studies. Here we examine the impact of batch effects on predictors built from genomic technologies. To investigate batch effects, we collected publicly available gene expression measurements with known outcomes, and estimated batches using date. Using these data we show (1) the impact of batch effects on prediction depends on the correlation between outcome and batch in the training data, and (2) removing expression measurements most affected by batch before building predictors may improve the accuracy of those predictors. These results suggest that (1) training sets should be designed to minimize correlation between batches and outcome, and (2) methods for identifying batch-affected probes should be developed to improve prediction results for studies with high correlation between batches and outcome.

Suggested Citation

  • Parker Hilary S. & Leek Jeffrey T., 2012. "The practical effect of batch on genomic prediction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-22, April.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:3:n:10
    DOI: 10.1515/1544-6115.1766
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    References listed on IDEAS

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    Cited by:

    1. Aline Talhouk & Stefan Kommoss & Robertson Mackenzie & Martin Cheung & Samuel Leung & Derek S Chiu & Steve E Kalloger & David G Huntsman & Stephanie Chen & Maria Intermaggio & Jacek Gronwald & Fong C , 2016. "Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-18, April.
    2. Charlotte Soneson & Sarah Gerster & Mauro Delorenzi, 2014. "Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-13, June.

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