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Statistical Evaluation of Labeled Comparative Profiling Proteomics Experiments Using Permutation Test.

Hien D Nguyen1,2, Geoffrey J McLachlan1, Michelle M Hill3

  • 1School of Mathematics and Physics,, The University of Queensland, St. Lucia, 4072, QLD, Australia.

Methods in Molecular Biology (Clifton, N.J.)
|December 16, 2016
PubMed
Summary
This summary is machine-generated.

This study presents a robust statistical method for analyzing comparative proteomics data. It details permutation analysis with false discovery rate control for accurate identification of differentially abundant proteins in non-normal SILAC datasets.

Keywords:
Comparative profilingFalse discovery rateHypothesis testPermutation testSILACSimultaneous testing

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical analysis in biological research

Background:

  • Comparative profiling proteomics experiments are crucial for inferring protein abundance levels from large-scale peptide measurements.
  • Accurate statistical evaluation is needed to identify differentially abundant proteins between experimental conditions.
  • Previous work identified non-normal distributions in SILAC (Stable Isotope Labeling by Amino acids in Cell culture) datasets.

Purpose of the Study:

  • To outline a superior statistical method for evaluating non-normal peptide ratio data from SILAC experiments.
  • To provide practical steps and R scripts for performing permutation analysis.
  • To implement false discovery rate control using the Benjamini-Yekutieli method.

Main Methods:

  • Utilized permutation testing as a superior statistical method for non-normal peptide ratio data.
  • Employed the Benjamini-Yekutieli method for false discovery rate control.
  • Provided R scripts for the practical implementation of the described analysis pipeline.

Main Results:

  • Demonstrated the effectiveness of permutation tests for statistically evaluating non-normal peptide ratios in SILAC data.
  • Successfully integrated false discovery rate control to manage statistical significance.
  • The described methodology offers a reliable approach for differential protein abundance analysis.

Conclusions:

  • Permutation analysis is a superior statistical approach for comparative proteomics datasets with non-normal distributions.
  • The R scripts and methodology facilitate accurate identification of differentially abundant proteins.
  • This approach enhances the reliability of findings in SILAC-based proteomics research.