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Examining the practical limits of batch effect-correction algorithms: When should you care about batch effects?

Longjian Zhou1, Andrew Chi-Hau Sue1, Wilson Wen Bin Goh2

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Batch effect correction algorithms (BECAs) show robustness in moderately confounded data but decline in performance when confounding is strong. Conventional normalization may outperform BECAs in challenging scenarios.

Keywords:
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Area of Science:

  • Bioinformatics
  • Genomics
  • Proteomics

Background:

  • Batch effects introduce technical variation, confounding biological analyses.
  • Existing studies often rank batch effect correction algorithms (BECAs) without considering context.

Purpose of the Study:

  • To evaluate the robustness of BECAs across varying degrees of confounding between sample classes and batch effects.
  • To determine the limits of BECA performance and their interaction with normalization methods.

Main Methods:

  • Simulated class and batch effects using two distinct methods.
  • Tested representative datasets from genomics (RNA-Seq) and proteomics.
  • Assessed BECA performance under moderate and strong confounding scenarios.

Main Results:

  • Most BECAs are robust under moderate confounding, with minimal impact from upstream normalization.
  • BECA performance degrades significantly under strong confounding, showing variable precision and recall.
  • Conventional normalization methods can outperform BECAs in strongly confounded scenarios and do not hinder downstream feature selection.

Conclusions:

  • The notion of a single 'best' BECA is context-dependent.
  • BECA performance has limits, particularly under strong confounding.
  • Effective batch effect correction does not guarantee optimal downstream analysis; conventional normalization may be preferable in some cases.