pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples

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, Kansas City, USA
Thompson Jeffrey A.: Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA
Koestler Devin C.: Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, USA

Statistical Applications in Genetics and Molecular Biology, 2022, vol. 21, issue 1, 14

Abstract: Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, “bridging samples”, to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer’s disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

Keywords: batch effect correction; longitudinal gene expression; power and sample size calculation; shinyR; temporal microarray data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/sagmb-2022-0003

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