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Tail-Robust Quantile Normalization.

Eva Brombacher1,2,3,4,5, Ariane Schad6, Clemens Kreutz1,3

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, 79104, Freiburg, Germany.

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|September 1, 2020
PubMed
Summary
This summary is machine-generated.

Mass spectrometry (MS) proteomics data contain systematic errors that obscure biological signals. Tail-robust quantile normalization (TRQN) improves upon standard methods by preserving tail signals and handling missing values.

Keywords:
PRIDEmissing valuesnormalizationproteomicsrank invariance

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • High-throughput biological data, including mass spectrometry (MS)-based proteomics, are susceptible to systematic, non-biological variance.
  • This variance complicates the identification of true biological signals, reducing statistical power and biasing the detection of differentially expressed proteins.
  • Current analysis workflows often use normalization methods, like quantile normalization (QN), to mitigate this variance before statistical analysis.

Purpose of the Study:

  • To introduce a novel normalization method that addresses limitations of classical quantile normalization in proteomics data.
  • To improve the estimation of biological signals, particularly for proteins with consistently high intensities (tail features).
  • To account for sample-dependent missing values (MVs) during normalization.

Main Methods:

  • Development and application of a novel method named tail-robust quantile normalization (TRQN).
  • TRQN is designed as an improvement over classical QN, specifically for quantitative MS data.
  • The method preserves biological signals in the tails of the protein intensity distribution and handles missing values.

Main Results:

  • TRQN effectively preserves biological signals from features in the tails of the intensity distribution, which are often misrepresented by standard QN.
  • The method successfully accounts for sample-dependent missing values, a common issue in proteomics datasets.
  • TRQN improves the accuracy of statistical inferences by providing a less biased estimation of variance.

Conclusions:

  • Tail-robust quantile normalization (TRQN) offers a significant advancement for analyzing quantitative MS proteomics data.
  • By preserving tail signals and managing missing values, TRQN enhances the reliability of identifying differentially expressed proteins.
  • This freely available approach improves the robustness of statistical analyses in the presence of systematic variance.