Comparison of Statistical Tests and Power Analysis for Phosphoproteomics Data

Area of Science:

• Proteomics and Bioinformatics
• Statistical Methods in Biological Research

Background:

• Quantitative proteome and phosphoproteome data generation is advancing rapidly.
• Effective target identification in case-control studies relies on robust statistical testing.

Purpose of the Study:

• To develop a simulation framework for realistic phospho-peptide data.
• To evaluate the performance of various statistical tests for differential expression analysis.

Main Methods:

• Developed a simulation framework for phospho-peptide data generation.
• Quantified performance of t-tests, Bayesian tests, and ranking-by-fold-change tests.
• Assessed impact of sample size, variance, and fold change on statistical power.

Main Results:

• Bayesian tests demonstrated superior performance over traditional t-tests by sharing variance information.
• Ranking-by-fold-change tests showed similar power but required permutation analysis to control Type I error rates.
• Two-sample Bayesian tests accounting for intensity-variance dependencies excelled with complex variance data.

Conclusions:

• Bayesian approaches offer advantages for analyzing large-scale proteomics and phosphoproteomics data.
• Model-informed experimental design and principled statistical analysis are essential for reliable findings.
• Balanced case-control groups and consideration of peptide standard deviations are recommended for optimal power.