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. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package 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 package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/ prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

Evaluating Proteomics Imputation Methods with Improved Criteria

“…We have shown that at the peptide and protein level, for DIA, label-free DDA and TMT experiments, no imputation generally works, as well as the most commonly used imputation methods. Our results are in line with Wolski et al, who suggest that statistical models of differential expression that do not impute, but rather explicitly model missingness, tend to outperform traditional models …”

Stepwise assembly and release of Tc toxins from Yersinia entomophaga

“…The R package prolfqua 63 was used to analyse differential expression and to determine group differences, confidence intervals and false discovery rates for all quantifiable proteins. Starting with the precursor abundances reported by DIA-NN, we determined protein abundances using the Tukeys-median polish.…”

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

“…prolfqua 23 ( ) integrates the basic steps of a differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The modular design of prolfqua enables users to select the optimal differential expression analysis algorithm.…”

“…Previous works have mainly focused on evaluating the software that performs peptide identification, protein inference, and the generation of protein intensity tables. ,, We first briefly describe the main packages and tools that enable the statistical analysis of LFQ data sets from peptide or protein intensity data. While multiple packages and tools are available for statistical analysis of these data, we selected some of the most relevant ones and novel implementations including MSstats, Perseus, Proteus, prolfqua, ProVision, LFQ-Analyst, Eatomics, ProStaR, and msqrob2. − Finally, we used three different data setsUPS spiked data set, large-scale mix data set and toxicology data setto evaluate the performance of each tool and discuss some of the advantages and disadvantages of their use with different types of data sets.…”