pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
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Qing Xia
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.
Funding source: National Institute of General Medical Sciences
Award Identifier / Grant number: P20 GM103418
Award Identifier / Grant number: P20 GM130423
Funding source: National Cancer Institute
Award Identifier / Grant number: P30 CA168524
Funding source: University of Kansas
Award Identifier / Grant number: Unassigned
Acknowledgments
We would like to extend our gratitude to members of the Statistical ‘Omics Working Group in the Department of Biostatistics & Data Science at the University of Kansas Medical Center for their constructive feedback on this manuscript. In particular, a special thanks to: Lisa Neums, Shachi Patel, Shelby Bell-Glenn, Whitney Shae, Bo Zhang, Samuel Boyd, Emily Nissen, Jonah Amponsah, Dr. Prabhakar Chalise, Dr. Jinxiang Hu, Dr. Lynn Chollet-Hinton, Dr. Nanda Yellapu, Dr. Dong Pei, and Dr. Mihaela Sardiu.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: Research reported was supported by: the National Cancer Institute (NCI) Cancer Center Support Grant P30 CA168524; the Kansas IDeA Network of Biomedical Research Excellence Bioinformatics Core, supported by the National Institute of General Medical Science award P20 GM103418; the Kansas Institute for Precision Medicine COBRE, supported by the National Institute of General Medical Science award P20 GM130423.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/sagmb-2022-0003).
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Artikel in diesem Heft
- Review Article
- Challenges for machine learning in RNA-protein interaction prediction
- Research Articles
- Distinct characteristics of correlation analysis at the single-cell and the population level
- pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
- Use of SVM-based ensemble feature selection method for gene expression data analysis
- A robust association test with multiple genetic variants and covariates
- Estimation of the covariance structure from SNP allele frequencies
- GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
- Sparse latent factor regression models for genome-wide and epigenome-wide association studies
