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A pre-processing pipeline to quantify, visualize, and reduce technical variation in protein microarray studies.

Sophie Bérubé1, Tamaki Kobayashi2, Amy Wesolowski2

  • 1Department of Biostatistics, Johns Hopkins University Bloomberg, School of Public Health, Baltimore, MD, USA.

Proteomics
|October 20, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new pre-processing pipeline to reduce technical variation in protein microarrays. The method enhances the biological signal for more accurate downstream analysis.

Keywords:
Bland-Atlman plotsmeasurement agreementnormalizationproteomics

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Area of Science:

  • Biotechnology
  • Proteomics
  • Bioinformatics

Background:

  • Technical variation is inherent in laboratory assays, potentially obscuring biological signals.
  • Protein microarrays are valuable for serum protein quantification but lack standardized methods for technical variation correction.
  • Existing methods for technical variation correction are less developed for protein microarrays compared to DNA microarrays.

Purpose of the Study:

  • To develop and evaluate a pre-processing pipeline to correct for common technical variation in protein microarrays.
  • To improve the extraction of biological signals from protein microarray data.
  • To assess the impact of pre-processing choices on downstream analyses.

Main Methods:

  • A novel pre-processing pipeline was developed, building on existing normalization techniques.
  • The pipeline incorporates controls to effectively reduce technical variation.
  • The method was validated using data from two protein microarray studies and through simulations.

Main Results:

  • The proposed pipeline successfully reduced technical variation in protein microarray data.
  • Pre-processing choices were shown to significantly influence protein-intensity based ranks.
  • These variations in ranks can impact the outcomes of downstream analyses.

Conclusions:

  • The developed pre-processing pipeline offers a robust approach to mitigate technical variation in protein microarrays.
  • Effective correction of technical variation is crucial for accurate biological interpretation of protein microarray data.
  • The findings highlight the importance of careful consideration of pre-processing steps in protein microarray analysis.