Multiple testing corrections in quantitative proteomics: A useful but blunt tool.

Area of Science:

• Proteomics
• Statistical analysis
• Bioinformatics

Background:

• Multiple testing corrections are standard for controlling the false discovery rate (FDR).
• Proteomics experiments often suffer from low statistical power due to small effect sizes and limited replicates.
• Conventional multiple testing methods can fail to identify true positives in low-power settings.

Purpose of the Study:

• To evaluate the effectiveness of standard multiple testing corrections in low-power proteomics.
• To identify alternative strategies for analyzing proteomics data with limited power.
• To highlight the unique challenges proteomics poses to statistical analysis.

Main Methods:

• Simulations were used to demonstrate the impact of low power on multiple testing corrections.
• Consideration of effect sizes and peptide-level data analysis were explored as alternatives.
• The study focused on medium-scale proteomics experiments.

Main Results:

• Multiple testing corrections can be overly conservative and miss true positives in low-power proteomics.
• Effect size considerations and peptide-level analyses showed potential for increased sensitivity.
• Standard methods may not be universally applicable without adaptation.

Conclusions:

• Proteomics presents specific challenges that can blunt the effectiveness of standard multiple testing corrections.
• Researchers should consider alternative or complementary methods beyond conventional FDR control.
• Statistical tools should be adapted to the realities of low-power biological data, rather than applied as a rigid standard.