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A Quantitative Fitness Analysis Workflow
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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
PubMed
Summary
This summary is machine-generated.

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.

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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.