1 Overview

The package MQmetrics (MaxQuant metrics) provides a workflow to analyze the quality and reproducibility of your proteomics mass spectrometry analysis from MaxQuant. Input data are extracted from several MaxQuant output tables (MaxQUant Summer School Output tables), and produces a pdf report. It includes several visualization tools to check numerous parameters regarding the quality of the runs. It also includes two functions to visualize the iRT peptides from Biognosys in case they were spiked in the samples.

2 Workflow

2.1 Install the package

You can install MQmetrics from Biocodunctor with:

if (!requireNamespace("BiocManager", quietly = TRUE))


You can install the development version from GitHub with:

# install.packages("devtools")

3 Data Input

After your MaxQuant run has finished, a folder named combined has been created. This folder should have at least two other folders within:

../combined/txt/ Containing all the tables.txt ../combined/proc/ Containing #runningTimes.txt

You just need the path to the combined folder and you will be able to start using MQmetrics.

MQPathCombined <- '/home/alvaro/Documents/MaxQuant/example5/combined/'

3.1 Generate a report

First you need to load the library.


Then you just need to use the generateReport() function. This function has parameters to control each of the function that it aggregates. You can read more about those parameters by using:


Though, the most important parameters are the following:

generateReport(MQPathCombined = , # directory to the combined folder
               output_dir = , # directory to store the resulting pdf
               long_names = , # If your samples have long names set it to TRUE
               sep_names = , # Indicate the separator of those long names
               UniprotID = , # Introduce the UniprotID of a protein of interest
               intensity_type = ,) # Intensity or LFQ

# The only mandatory parameter is MQPathCombined, the rest are optional.

Is as simple as this to use MQmetrics:

# If you're using a Unix-like OS use forward slashes.
MQPathCombined <- '/home/alvaro/Documents/MaxQuant/example5/combined/'

# If you're using Windows you can also use forward slashes:
MQPathCombined <- "D:/Documents/MaxQuant_results/example5/combined/"

#Use the generateReport function.

3.2 Visualization plots

If you are only interested in a few plots from the generateReport() function, you can do it. You only need to have access to each file independently.

3.2.1 Load the data

MQmetrics requires 8 tables from the MaxQuant analysis and the #runningTimes file. If you want to learn more about the information of each of these tables, you can do so in the MaxQuant Summer School videos.

# To create the vignettes and examples I use data that is in the package itself:
MQPathCombined <- system.file('extdata/combined/', package = 'MQmetrics')

# make_MQCombined will read the tables needed for creating the outputs.
MQCombined <- make_MQCombined(MQPathCombined, remove_contaminants = TRUE) 

3.2.2 MaxQuant Analysis Parameters

The MaxQuant output directory is: 
The MaxQuant analysis  started the day: 16/04/2021 at the time: 18:07:23.
The whole  MaxQuant analysis lasted: 0 days, 2 hours and 15 mins.
The MaxQuant analysis finished on: 16/04/2021 at: 20:22:40
The MaxQuant version used was:
The user was: thomas.stehrer
The machine name was: FGU045PC004
The PSM FDR was: 0.01
The protein FDR was: 0.01
The match between runs was: True
The fasta file(s) used was: 
The iBAQ presence is: False
The PTM selected is/are: Oxidation (M);Acetyl (Protein N-term)

4 Visualizations.

4.1 Proteins Identified

The function PlotProteinsIdentified(), will take as input the proteinGroups.txt table and show the number of proteins and NAs in each sample. It can differentiate two types of intensities: ‘Intensity’ or ‘LFQ’.

                       long_names = TRUE,
                       sep_names = '_',
                       intensity_type = 'LFQ',
                       palette = 'Set2')
#> Warning in stri_count_regex(string, pattern, opts_regex = opts(pattern)):
#> argument is not an atomic vector; coercing

#> Warning in stri_count_regex(string, pattern, opts_regex = opts(pattern)):
#> argument is not an atomic vector; coercing

4.2 Peptides Identified

The function PlotPeptidesIdentified(), will take as input the summary table and show the number of peptides sequences identified in each sample.

                       long_names = TRUE,
                       sep_names = '_', 
                       palette = 'Set3')

4.3 Proteins versus peptide/protein ratio

The function PlotIdentificationRatio(), will take as input the summary and proteinGroups tables and plot the number of protein found vs the ratio of peptides/proteins found in each Experiment.

                      intensity_type = 'LFQ',
                      long_names = TRUE,
                      sep_names =  '_')

4.4 MS/MS submitted versus identified

The function PlotMsMs(), will take as input the summary.txt table and show the number of MS/MS Submitted and identified in each sample.

       long_names = TRUE, 
       sep_names = '_',
       position_dodge_width = 1,
       palette = 'Set2')

4.5 Peaks submitted versus identified

The function PlotPeaks(), will take as input the summary.txt table and show the number peaks detected and sequenced in each sample.

        long_names = TRUE,
        sep_names = '_',
        palette = 'Set2')

4.6 Isotope patterns detected and sequenced

The function PlotIsotopePattern(),will take as input the summary.txt table and show the number isotope patterns detected and sequenced in each sample.

                 long_names = TRUE,
                 sep_names = '_',
                 palette = 'Set2')