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prolfqua - a comprehensive R package for Proteomics Differential Expression Analysis

The R package contains functions for analyzing mass spectrometry based experiments. This package is developed at the FGCZ. The package documentation including vignettes can be accessed at https://fgcz.github.io/prolfqua/index.html

prolfqua makes easy things easy while remaining fully hackable.

How to install prolfqua?

Requirements : A Windows|Linux|MacOSX platform with R (>= 4.1) installed.

We recommend to install the package using the latest release Download the prolfqua_X.Y.Z.tar.gz from the github release page into your working directory. and then execute:

install.packages("./prolfqua_X.Y.Z.tar.gz",repos = NULL, type="source")

To install the package without vignettes from github you can execute in R.

install.packages('remotes')
remotes::install_github('fgcz/prolfqua', dependencies = TRUE)

If you want to build the vignettes on your system:

install.packages('remotes')
remotes::install_github('fgcz/prolfqua', build_vignettes = TRUE, dependencies = TRUE)

Let us please know about any installation problems or errors when using the package: https://github.com/fgcz/prolfqua/issues

How to get started

How to build a LFQData object from a table with protein or peptide quantification results, and a table with sample annotation is described in more detail here the: CreatingConfigurations vignette

A minimal example for a table with protein abudances is:

#Table with abundances
df <- data.frame(protein_Id = c("tr|A|HUMAN","tr|B|HUMAN","tr|C|HUMAN","tr|D|HUMAN"),
                 Intensity_A = c(100,10000,10,NA),
                 Intensity_B = c(NA, 9000, 20, 100),
                 Intensity_C = c(200,8000,NA,150),
                 Intensity_D = c(130,11000, 50, 50))
# Table with sample annotation
annot <- data.frame(Sample = c("Intensity_A", "Intensity_B", "Intensity_C", "Intensity_D"), Group = c("A","A","B","C"))

# convert into long format
table_long <- tidyr::pivot_longer(df, starts_with("Intensity_"),names_to = "Sample", values_to = "Intensity")

table_long <- dplyr::inner_join(annot, table_long)

# create TableAnnotation and AnalysisConfiguration

config <- prolfqua::AnalysisConfiguration$new()
config$file_name = "Sample"
config$work_intensity = "Intensity"
config$hierarchy[["protein_Id"]]    <-  "protein_Id"
config$factors[["Group"]] <- "Group"

# Build LFQData object
analysis_data <- prolfqua::setup_analysis(table_long, config)
lfqdata <- prolfqua::LFQData$new(analysis_data, config)
lfqdata$hierarchy_counts()

Once you have created an LFQData you can use prolfqua like this.

R.version.string; packageVersion("prolfqua")

## here we simulate peptide level data
startdata <- sim_lfq_data_peptide_config()
lfqpep <- LFQData$new(startdata$data, startdata$config)


## transform intensities
lfqpep <- lfqpep$get_Transformer()$log2()$robscale()$lfq
lfqpep$rename_response("log_peptide_abundance")
agr <- lfqpep$get_Aggregator()
lfqpro <- agr$medpolish()
lfqpro$rename_response("log_protein_abundance")

## plot Figure 3 panels A-D
pl <- lfqpep$get_Plotter()
panelA <- pl$intensity_distribution_density() +
  ggplot2::labs(tag = "A") + ggplot2::theme(legend.position = "none")
panelB <- agr$plot()$plots[[1]] + ggplot2::labs(tag = "B")
panelC <- lfqpro$get_Stats()$violin() + ggplot2::labs(tag = "C")
pl <- lfqpro$get_Plotter()
panelD <- pl$boxplots()$boxplot[[1]] + ggplot2::labs(tag = "D")
ggpubr::ggarrange(panelA, panelB, panelC, panelD)

## specify model
modelFunction <-
 strategy_lm("log_protein_abundance  ~ group_")

## fit models to lfqpro data
mod <- build_model(
 lfqpro,
 modelFunction
)

## specify contrasts
Contr <- c("AvsCtrl" = "group_A - group_Ctrl",
     "BvsCtrl" = "group_B - group_Ctrl",
     "BvsA" = "group_B - group_A"
      )
      
## determine contrasts and plot
contrastX <- prolfqua::Contrasts$new(mod, Contr)
pl <- contrastX$get_Plotter()
pl$volcano()$FDR

Watch the silico talks
See our article at the Journal of Proteome Research
See Bioconductor 2021 Conference poster.
Watch the lightning (8 min) talk at EuroBioc2020 on YouTube or slides.
Read the pkgdown generate website https://fgcz.github.io/prolfqua/index.html

Detailed documentation with R code:

Document's explaining how to run an analysis with prolfqua are at github.io https://fgcz.github.io/prolfqua/index.html.

Example QC and sample size report

QC and sample size Report

Releated projects

prolfquabenchmark - a package to document the performance of prolfqua, MSstats, msqrob, and proda. See documentation: [https://prolfqua.github.io/prolfquabenchmark/]
prolfquapp: Generating Dynamic DEA Reports with the prolfqua R Package https://github.com/prolfqua/prolfquapp
prophosqua - (scripts for the analysis of phospho experiments) https://github.com/prolfqua/prophosqua

How to cite?

Please do reference the prolfqua article at Journal of Proteome Research


@article{prolfquawolski2023,
author = {Wolski, Witold E. and Nanni, Paolo and Grossmann, Jonas and d’Errico, Maria and Schlapbach, Ralph and Panse, Christian},
title = {prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis},
journal = {Journal of Proteome Research},
volume = {4},
number = {22},
pages = {1092–1104},
year = {2023},
doi = {10.1021/acs.jproteome.2c00441},
    note = {PMID: 36939687},
URL = {https://doi.org/10.1021/acs.jproteome.2c00441},
eprint = {https://doi.org/10.1021/acs.jproteome.2c00441}
}

Motivation

The package for proteomics label free quantification prolfqua (read : prolevka) evolved from a set of scripts and functions written in the R programming language to visualize and analyze mass spectrometric data, and some of them are still in R packages such as quantable, protViz or imsbInfer. For computing protein fold changes among treatment conditions, we first used t-test or linear models, then started to use functions implemented in the package limma to obtain moderated p-values. We did also try to use other packages such as MSStats, ROPECA or MSqRob all implemented in R, with the idea to integrate the various approaches to protein fold-change estimation. Although all these packages were written in R, model specification, input and output formats differ widely and wildly, which made our aim to use the original implementations challenging. Therefore, and also to understand the algorithms used, we attempted to reimplement those methods, if possible.

When developing prolfqua we were inspired by packages such as sf or stars which use data in long table format and dplyr for data transformation and ggplot2 for visualization. In the long table format each column stores a different attribute, e.g. there is only a single column with the raw intensities. In the wide table format there might be several columns with the same attribute, e.g. for each recorded sample a raw intensity column. In prolfqua the data needed for analysis is represented using a single data-frame in long format and a configuration object. The configuration annotates the table, specifies what information is in which column. The results of the statistical modelling are stored in data frames. Relying on the long data table format enabled us to access a large variety of useful visualizations as well as data preprocessing methods implemented in the R packages dplyr and ggplot2.

The use of an annotated table makes integrating new data if provided in long formatted tables simple. Hence for Spectronaut or Skyline text output, all is needed is a table annotation (see code snipped). Since MSStats formatted input is a table in long format prolefqa works with MSstats formatted files. For software, which writes the data in a wide table format, e.g. Maxquant, we implemented methods which first transfo

Prolfqua

Install / Use

README