Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
6 Issues per year
IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191
CiteScore 2016: 0.94
SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596
Mathematical Citation Quotient (MCQ) 2016: 0.06
The practical effect of batch on genomic prediction
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.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.