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Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using BatchQC

Year: 2026
Type: preprint
Abstract: Batch effect correction is a common and often necessary step in data analysis to reduce bias due to technical and experimental factors when combining multiple batches of data. The severity of the batch effects dictates the correction strategy; therefore, a careful assessment of each dataset's batch effects is necessary. BatchQC is an R package that provides reproducible tools and visualizations for quantitatively and qualitatively addressing batch effects across a broad range of data types. BatchQC integrates with standardized Bioconductor data structures and features an object-oriented design, enabling the application of workflows that can freely evaluate and process data within and outside the package tools. Common batch evaluation methods, along with novel quantitative metrics, help determine the benefits of batch correction for each dataset and enable direct comparisons between methods. Here, we present BatchQC as the first comprehensive batch-correction R package, with independent tools, reproducible workflows, visualization, and novel statistics.
DOI: 10.64898/2026.02.12.705115
Authors: Jessica K Anderson, Jiwei Zhang, Xinshou Ge, Howard Fan, Yaoan Leng, Michael Silverstein, Regan Conrad, Zhaorong Li, Evan Holmes, Solomon S. Joseph, Sean Lu, Russell T Shinohara, Tenglong Li, W. Evan Johnson, Alzheimer’s Disease Neuroimaging Initiative
Language: English
Open Access status: green
Funders: U.S. Department of Defense, National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Human Genome Research Institute, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering
OpenAlex ID: W7128916803
Cites: 31
Cited by: 1
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