prolfqua – R-package for proteomics label-free quantification using linear models
Creators: name: Wolski, Witold
name: Panse, Christian
name: Grossmann, Jonas
name: D'Errico, Maria
name: Nanni, Paolo
Publisher: name: F1000 Research Limited
Year: 2021

description: We use `prolfqua` to develop highly customizable, visually appealing, and interactive data analysis reports in pdf or HTML format for quantification experiments. We use `prolfqua` to visualize and model simple experimental designs with a single explanatory variable and complex experiments with multiple factors. The `prolfqua` package integrates essential steps of the data analysis workflow: quality control, data normalization, protein aggregation, sample size estimation, modeling, and hypothesis testing. We further use `prolfqua` to benchmark data acquisition, data preprocessing or data modeling methods. We developed and improved the package by applying the "Eating your own dog food" principle, making it easy to use.<br> <br> We store all the data needed for analysis in a single data frame in a tidy table, i.e., every column is a variable, every row is an observation, every cell is a single value. Using an R6 configuration object, we specify what variable is in which column, making it easy to integrate new inputs in prolfqua if provided in tidy tables. For example, to visualize tidy Spectronaut, or Skyline outputs, or data in MSStats format, only a few lines of code to update the prolfqua configuration are needed. For popular software like MaxQuant or MSFragger, which stores the same variable (e.g., intensity) in multiple columns, one for each sample, we implemented methods that transform the data into tidy tables. Relying on the tidy data table enabled us to easily interface with many data manipulation, visualization, and modeling methods implemented in base R and the tidyverse.<br> <br> We use R's linear model and mixed model formula in prolfqua. R linear model and linear mixed effect models allow modeling parallel designs, repeated measurements, factorial designs, and many more. R's formula interface for linear models is flexible, widely used, and well documented. This approach makes it easy to reproduce an analysis performed with prolfqua in any other statistical programming language. We implemented features specific to high throughput experiments, such as the experimental Bayes variance and p-value moderation, which utilizes the parallel structure of the protein measurements and the analysis [limma]. We also compute probabilities of differential protein regulation based on peptide level models [ropeca]. Contrasts to test hypothesis can intuitively be specified in prolfqua using descriptive variable names, e.g., “Treatment_drug - Treatment_placebo”. <br> The Benchmark functionality of prolfqua includes ROC curves and computes partial areas under those curves (pAUC) and other scores. We use it to study how well linear, mixed effect models or p-value moderation models quantitative mass spectrometric high throughput experiments.<br> The benchmarking results enable us to choose the best method depending on the number of missing values present or the research hypothesis. Based on these results, we discuss how much each of these Mass Spectrometric, computational and statistical methods can improve the sensitivity and specificity of the analysis.<br> How to install and use prolfqua to analyze your data, or benchmark your MS method or software, is shown in various tutorials available at https://wolski.github.io/prolfqua/.<br> <br> Prolfqua is an easy-to-use R package to analyze quantitative mass spectrometric data and to report results. We used it here to benchmark MS software and statistical methods.