prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Area of Science:

• Proteomics
• Bioinformatics
• Statistical Computing

Background:

• Mass spectrometry is crucial for quantitative proteomics and differential protein expression analysis.
• Existing software tools offer diverse quantification and analysis options.
• A need exists for a unified, user-friendly R interface for statistical procedures in proteomics.

Purpose of the Study:

• To develop a modular R package, prolfqua, for mass spectrometry-based differential expression analysis.
• To integrate quality control, normalization, statistical modeling, and hypothesis testing.
• To facilitate comparison and understanding of various statistical methods in proteomics.

Main Methods:

• The prolfqua package integrates a comprehensive workflow for mass spectrometry data analysis.
• It supports simple and complex experimental designs with multiple factors.
• Includes functionality for benchmarking different data acquisition, preprocessing, and modeling methods.

Main Results:

• The prolfqua package provides a clear, consistent, and discoverable API for R.
• It enables sensitive and specific differential expression analysis.
• Offers tools for quality control, normalization, protein aggregation, statistical modeling, and sample size estimation.

Conclusions:

• The prolfqua R package streamlines proteomics data analysis, improving reproducibility and accessibility.
• It supports diverse experimental designs and facilitates method comparison.
• The package is available on GitHub under an MIT license for broad use.