Prolfqua - R package for visualization and modelling of proteomics label-free quantification data
Witold Eryk Wolski,Christian Panse FGCZ
Abstract
The R-package for \textbf{pro}teomics \textbf{l}abel \textbf{f}ree \textbf{qua}ntification \texttt{prolfqua} (read: prolewka) evolved from functions and code snippets used to visualize and analyze label-free quantification data. To compute protein fold changes among treatment conditions, we first used t-test, linear models, or functions implemented in the package limma. We evaluated \href{10.18129/B9.bioc.MSstats}{MSStats}, \href{10.1038/s41598-017-05949-y}{ROPECA} or \href{https://github.com/statOmics/MSqRob}{MSqRob} all implemented in R, with the idea to integrate the various approaches in our analysis pipeline. Although these packages are implemented in R, model specification, input, and output formats differ widely and wildly. Furthermore, to better understand the inferences produced, we decided to use the R linear and mixed effect models directly and transparently and to reimplement some features from the other packages when considered useful. The R-package prolfqua is the outcome of this venture.
The \emph{prolfqua} package integrates most of the steps of the LFQ data analysis workflow: quality control, data normalization, protein aggregation, sample size estimation, modeling, and hypothesis testing. For instance, the quality control module makes it easy to determine the coefficients of variations (CV) for peptides and proteins within conditions, summarize, and visualize missing data or intensities.
When developing \emph{prolfqua}, we draw inspiration from packages such as \emph{sf}, which uses data in a long tidy table format. We represent the data needed for analysis using a single data-frame in long format and an R6 configuration object. Using the configuration object, we specify what data is in which column, making it easy to integrate new inputs if provided in long tidy tables. All that is needed to incorporate Spectronaut, Skyline text output, or the \textbf{MSStats} data format is a few code lines of code to update the configuration object. For software like MaxQuant or MSFragger, which writes the data into a wide table, with several intensity columns, one for each sample, we implemented methods that transform the data into a long tidy table. Relying on the long tidy data table enabled us to easily interface with many data manipulation, visualization, and modeling methods implemented in base R and the \emph{tidyverse}.
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. We integrate R’s linear model and mixed model formula interface in \emph{prolfqua}’s API. This glass box approach should make it easy to reimplement an analysis performed with prolfqua using base R or other programming languages by reading the analysis script and without looking at the package code. Knowledge of the R regression model infrastructure is an advantage when using our package. Acknowledging the formula interface’s complexity and the popularity of MSstats, we provided functionality to derive the model formula from an MSstats data format.
The prolfqua package fits the linear model to all the proteins in the dataset. Afterward, we can compute contrast to test the hypothesis, perform ANOVA, or model selection using the likelihood ratio test for thousands of proteins. By exploiting the parallel structure of the analysis, \emph{prolfqua} implements t-statistic moderation to improve inference for small sample sizes {[}\emph{limma}{]}. It also computes probabilities of differential protein regulation based on peptide level models {[}\emph{ropeca}{]}.
To optimize a data analysis workflow, prolfqua implements and integrates benchmarking methods, e.g., computing ROC curves. We implemented these methods to assess differences between linear models, mixed effect models, p-value moderation, or Bayesian regression models using the IonStar dataset.
We developed and improved the package by applying the ``Eating your own dog food'' principle. We used it to analyze SRM, PRM, and LFQ data and to generate analysis reports. The current iteration of the packages API groups the functionality into R6 classes (e.g., LFQPlotter, LFQSummarizer, LFQStats, Model, Contrast, etc.). We choose R6 for its visual debugging and code completion support in RStudio.
We believe that the package makes it easy to perform proteomics quantitative data analysis, generate visualizations and integrate them into reproducible reports using Rmarkdown. You can install the package from \url{www.github.com/wolski/prolfqua}, and we are working on making it available on Bioconductor.
Keywords: proteomics,quantification,modelling,quality control,inference