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Related Experiment Video

Updated: Oct 23, 2025

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Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Jelena Čuklina1,2,3, Chloe H Lee1, Evan G Williams1,4

  • 1Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.

Molecular Systems Biology
|August 25, 2021
PubMed
Summary

Batch effects are technical variability in large proteomic studies. This protocol offers methods and an R package ("proBatch") to assess, normalize, and correct batch effects, ensuring robust biological signal extraction.

Keywords:
batch effectsdata analysislarge-scale proteomicsnormalizationquantitative proteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Systems Biology

Background:

  • Large-scale mass spectrometry-based proteomics experiments generate substantial data.
  • Technical variability, known as batch effects, can confound results in high-throughput studies.
  • Batch effects obscure true biological signals in complex proteomic datasets.

Purpose of the Study:

  • To present a comprehensive protocol for managing batch effects in large-scale proteomic data.
  • To provide practical solutions for assessing, normalizing, and correcting batch effects.
  • To enhance the reliability and reproducibility of clinical proteomics and systems biology research.

Main Methods:

  • Review of established methodologies for batch effect correction from related fields.
  • Development of solutions tailored to proteomic data challenges, including ion intensity drift and missing values.
  • Implementation of quality control techniques for assessing batch effect adjustment.

Main Results:

  • A step-by-step protocol for batch effect assessment, normalization, and correction is detailed.
  • An R package, "proBatch", is provided, containing essential functions for the protocol.
  • The methodology was successfully demonstrated on five diverse proteomic datasets with hundreds of samples each.

Conclusions:

  • The developed protocol and tools improve the robustness and transparency of biological signal extraction from large proteomic studies.
  • This approach facilitates more reliable and reproducible research in clinical proteomics and systems biology.
  • Effective batch effect management is crucial for leveraging the statistical power of large-scale proteomic datasets.