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Mapping Dysfunctional Protein-Protein Interactions in Disease
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An effect size filter improves the reproducibility in spectral counting-based comparative proteomics.

Josep Gregori1, Laura Villarreal, Alex Sánchez

  • 1Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona (UAB), Barcelona, Spain; Statistics Department, University of Barcelona (UB), Barcelona, Spain.

Journal of Proteomics
|June 18, 2013
PubMed
Summary

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Using an effect size filter in proteomics improves biomarker discovery by increasing true positives and reducing false positives. This enhances the reproducibility of comparative proteomic analysis results.

Area of Science:

  • Proteomics
  • Biomarker Discovery
  • Statistical Analysis

Background:

  • Low reproducibility in gene expression studies linked to p-value reliance.
  • Effect size criteria recommended to complement p-values for biomarker discovery.

Purpose of the Study:

  • Evaluate the impact of an effect size filter on spectral counting-based comparative proteomic analysis.
  • Improve reproducibility in proteomic biomarker discovery.

Main Methods:

  • Applied an effect size filter to spectral counting data.
  • Utilized simulation experiments to validate findings.
  • Recommended specific thresholds for log2 fold change and signal level.

Main Results:

  • Effect size filter increased true positives.
Keywords:
Effect sizeFeature filtersPoissonQSpecQuasi-likelihoodedgeR

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  • Effect size filter decreased false positives and false discovery rate.
  • Relaxing p-value cutoffs with effect size filtering enhances reproducibility.

    • Conclusions:

    • Effect size post-test filtering significantly improves statistical results in quantitative proteomics.
    • Feature filtering approaches enhance reproducibility across laboratories and platforms.
    • Recommended minimum absolute log2 fold change of 0.8 and 2-4 SpC signal for comparative proteomics.