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How missing value imputation is confounded with batch effects and what you can do about it.

Wilson Wen Bin Goh1, Harvard Wai Hann Hui2, Limsoon Wong3

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore; Center for Biomedical Informatics, Nanyang Technological University, Singapore.

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This summary is machine-generated.

Batch effect correction and missing value imputation are interdependent data processing steps. Understanding their interaction improves data quality and reduces false discoveries in advanced modeling.

Keywords:
batch effectsclass-batch proportion imbalancecomputational biologyconfoundingdata sciencemissing value imputationstatistics

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Area of Science:

  • Data Science
  • Bioinformatics
  • Computational Biology

Background:

  • Data processing pipelines rely on sequential steps where upstream processes impact downstream ones.
  • Batch effect correction (BEC) and missing value imputation (MVI) are critical for data suitability in advanced modeling and reducing false discoveries.
  • The interactions between BEC and MVI are not extensively studied but are fundamentally interdependent.

Purpose of the Study:

  • To elucidate the interconnectedness and interdependence of BEC and MVI.
  • To demonstrate how batch sensitization can enhance MVI quality.
  • To introduce the concept of batch effect-associated missing values (BEAMs) and discuss mitigation strategies for batch-class imbalance.

Main Methods:

  • Conceptual discussion of BEC-MVI interactions.
  • Demonstration of batch sensitization's impact on MVI.
  • Exploration of machine learning techniques for addressing batch-class imbalance.

Main Results:

  • Batch sensitization improves the quality of missing value imputation.
  • Accounting for missingness enhances batch effect estimation during correction.
  • Batch effect-associated missing values (BEAMs) are identified as a key area of interaction.
  • Machine learning concepts can mitigate batch-class imbalance issues.

Conclusions:

  • BEC and MVI are interdependent, and their interaction significantly impacts data quality.
  • Batch sensitization offers a method to improve MVI, while considering missingness aids BEC.
  • Addressing BEAMs and batch-class imbalance is crucial for robust data processing pipelines.