Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE)

Xia Qing, Thompson Jeffrey A. and Koestler Devin C. ()
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Xia Qing: Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
Thompson Jeffrey A.: Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
Koestler Devin C.: Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA

Statistical Applications in Genetics and Molecular Biology, 2021, vol. 20, issue 4-6, 101-119

Abstract: Batch-effects present challenges in the analysis of high-throughput molecular data and are particularly problematic in longitudinal studies when interest lies in identifying genes/features whose expression changes over time, but time is confounded with batch. While many methods to correct for batch-effects exist, most assume independence across samples; an assumption that is unlikely to hold in longitudinal microarray studies. We propose Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE), a three-step parametric empirical Bayes approach that leverages technical replicate samples profiled at multiple timepoints/batches, so-called “bridge samples”, to inform batch-effect reduction/attenuation in longitudinal microarray studies. Extensive simulation studies and an analysis of a real biological data set were conducted to benchmark the performance of BRIDGE against both ComBat and longitudinal ComBat. Our results demonstrate that while all methods perform well in facilitating accurate estimates of time effects, BRIDGE outperforms both ComBat and longitudinal ComBat in the removal of batch-effects in data sets with bridging samples, and perhaps as a result, was observed to have improved statistical power for detecting genes with a time effect. BRIDGE demonstrated competitive performance in batch effect reduction of confounded longitudinal microarray studies, both in simulated and a real data sets, and may serve as a useful preprocessing method for researchers conducting longitudinal microarray studies that include bridging samples.

Keywords: batch effect correction; COMBAT; longitudinal gene expression; temporal microarray data (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/sagmb-2021-0020

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