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Reproducible Tools and Enhanced Computational Workflows for Batch Effect Evaluation of High-Throughput Data Using
Jessica K Anderson1, Jiwei Zhang2, Xinshou Ge3
1Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, 07103, USA.
Biorxiv : the Preprint Server for Biology
|February 23, 2026
Summary
Batch effect correction is crucial for reliable data analysis. BatchQC is a new R package offering tools and visualizations to assess and correct batch effects across diverse data types.
Area of Science:
- Bioinformatics
- Computational Biology
- Data Science
Background:
- Batch effects introduce bias in multi-batch data analysis.
- Assessing batch effect severity is critical for selecting correction strategies.
- Existing tools lack comprehensive, reproducible batch effect evaluation.
Purpose of the Study:
- Introduce BatchQC, a novel R package for batch effect assessment and correction.
- Provide reproducible tools and visualizations for quantitative and qualitative batch effect analysis.
- Facilitate informed decisions on batch correction strategies.
Main Methods:
- Developed BatchQC as an R package with an object-oriented design.
- Integrated standardized Bioconductor data structures for broad compatibility.
- Implemented common batch evaluation methods alongside novel quantitative metrics.
Main Results:
- BatchQC offers reproducible workflows for evaluating batch effects.
- Provides visualizations for qualitative and quantitative assessment.
- Novel metrics enable direct comparison of batch correction methods.
Conclusions:
- BatchQC is the first comprehensive R package for batch correction.
- Facilitates reproducible assessment and correction of batch effects.
- Aids in determining the benefits of batch correction for diverse datasets.
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