Journal
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
Volume 11, Issue 3, Pages -Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/1544-6115.1766
Keywords
batch effects; prediction; microarrays; reproducibility; research design
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Funding
- NCRR NIH HHS [R01 RR021967] Funding Source: Medline
- NIGMS NIH HHS [R01 GM103552, R01 GM083084] Funding Source: Medline
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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.
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