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

Abstract

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. Nevertheless, there is a need for a flexible and easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures. The prolfqua package can model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. It integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The application programmer interface strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis easy and exciting. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard dataset. Finally, we show that the implemented methods allow sensitive and specific differential expression analysis. The prolfqua R package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT licence, and runs on all platforms supported by the R free software environment for statistical computing and graphics.