Contents

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))
    install.packages("BiocManager")


BiocManager::install("MQmetrics")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("svalvaro/MQmetrics")

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.

library(MQmetrics)

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:

?generateReport

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.
generateReport(MQPathCombined)

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

MaxQuantAnalysisInfo(MQCombined) 
The MaxQuant output directory is: 
/tmp/RtmpJxcl9e/Rinst1841cf105b0e6c/MQmetrics/extdata/combined/
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: 1.6.17.0
The user was: thomas.stehrer
The machine name was: FGU045PC004
Number of threads: 4
The PSM FDR was: 0.01
The protein FDR was: 0.01
The match between runs was: True
The fasta file(s) used was: 
C:\MaxQuant_Databases\iRT_peptides_Biognosys_irtfusion.fasta
C:\MaxQuant_Databases\UP000005640_9606.fasta
The iBAQ presence is: False
The PTM selected is/are: Oxidation (M);Acetyl (Protein N-term)
Fixed modifications:             Carbamidomethyl (C)

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’.

PlotProteinsIdentified(MQCombined,
                       long_names = TRUE,
                       sep_names = '_',
                       intensity_type = 'LFQ',
                       palette = 'Set2')

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.

PlotPeptidesIdentified(MQCombined, 
                       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.


PlotProteinPeptideRatio(MQCombined,
                      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.

PlotMsMs(MQCombined,
       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.

PlotPeaks(MQCombined,
        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.

PlotIsotopePattern(MQCombined,
                 long_names = TRUE,
                 sep_names = '_',
                 palette = 'Set2')

4.7 Charge-state of the precursor ions

The function PlotCharge(), will take as input the evidence.txt table and show the charge-state of the precursor ion in each sample.

PlotCharge(MQCombined, 
         palette = 'Set2',
         plots_per_page = 6)
#> [[1]]

4.8 Protease Specificity

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

PlotProteaseSpecificity(MQCombined, 
                      palette = 'Set2',
                      plots_per_page = 6)
#> [[1]]

4.9 Peptide Hydrophobicity

The function PlotHydrophobicity(), takes as input the peptides.txt table and returns the distribution of GRAVY score.

PlotHydrophobicity(MQCombined,
                 show_median =  TRUE,
                 binwidth = 0.1, 
                 size_median = 1.5, 
                 palette = 'Set2',
                 plots_per_page = 6)
#> [[1]]

4.10 Andromeda Score

The function PlotAndromedaScore(), takes as input the peptides.txt table and returns the distribution of MaxQuant’s Andromeda Score.

PlotAndromedaScore(MQCombined,
                 palette = 'Set2',
                 plots_per_page = 6)
#> [[1]]

4.11 Protein intensities comparison

The function PlotIntensity(), takes as input the proteinGroups.txt table and returns a violin plot for those intensities. If the ‘LFQ’ intensities are in the proteinGroups.txt table, it will by default split the violion into "LFQ’ and ‘Intensity’. The parameter split_violin_intensity, can be set to FALSE and then select wether you would like to visualize the ‘Intensity’ or ‘LFQ’ intensity individually. If split_violin_intensity is set TRUE, but no LFQ intensities are not present, it will automatically show the normal Intensities.

PlotIntensity(MQCombined, 
            split_violin_intensity = TRUE, 
            intensity_type = 'LFQ', 
            log_base = 2, 
            long_names = TRUE, 
            sep_names = '_', 
            palette = 'Set2')

4.12 Protein PCA

The function PlotPCA() takes as input the proteinGroups.txt table and creates a Principal Componente Analysis plot of each Experiment.

PlotPCA(MQCombined,
      intensity_type = 'LFQ',
      palette = 'Set2')

4.13 Dynamic Range (Combined)

The function PlotCombinedDynamicRange() takes as input the proteinGroups.txt table and returns the dynamic range of all experiments combined. If the parameter show_shade is used, a square will appear showing the percent_proteins selected and the orders of abundance.

PlotCombinedDynamicRange(MQCombined,
                       show_shade = TRUE,
                       percent_proteins = 0.90)

4.14 Dynamic Range (Individual)

The function PlotAllDynamicRange() takes as input the proteinGroups.txt table and returns the dynamic range of all experiments separated. If the parameter show_shade is used, a square will appear showing the percent_proteins selected and the orders of abundance.

PlotAllDynamicRange(MQCombined,
                  show_shade = TRUE, 
                  percent_proteins = 0.90)

4.15 Protein Overlap

The function PlotProteinOverlap() takes as input the proteinGroups.txt table and returns a plot that shows the number of proteins identified in the samples.

PlotProteinOverlap(MQCombined)

4.16 Protein Coverage

The function PlotProteinCoverage() takes as input the peptides.txt and proteinGroups.txt tables and one or multiple protein UniprotID(s). It shows, if present, the coverage of that protein in each of the samples.

PlotProteinCoverage(MQCombined,
                  UniprotID =  c('P55072', 'P13639'),
                  log_base = 2,
                  segment_width = 1,
                  palette = 'Set2',
                  plots_per_page = 6)
#> [[1]]
#> [[1]][[1]]

#> 
#> 
#> [[2]]
#> [[2]][[1]]

4.17 iRT peptides (1)

The function PlotiRT() takes as input the evidence.txt table and returns, if found the iRT peptides from Biognosys. Their retention time and intensity.

PlotiRT(MQCombined, 
      show_calibrated_rt = FALSE,
      tolerance = 0.001,
      plots_per_page = 6)
#> [[1]]

4.18 iRT peptides (2)

The function PlotiRTScore() takes as input the evidence.txt table and returns, if found, a linear regression of the retention times of the iRT peptides of Biognosys.

PlotiRTScore(MQCombined, 
           tolerance = 0.001,
           plots_per_page = 6)
#> [[1]]

4.19 Total Ion Current

The function PlotTotalIonCurrent() takes as input the msmsScans.txt, and returns a plot showing the TIC values vs the retention time of each sample. It can show as well the maximum value of each sample.

PlotTotalIonCurrent(MQCombined,
                  show_max_value = TRUE,
                  palette = 'Set2',
                  plots_per_page = 6)
#> [[1]]

4.20 Acquisition Cycle

The function PlotAcquisitionCycle takes as input the msScans.txt table and returns the cycle time and MS/MS count vs the retention time of each sample. Note: msScans.ttxt is not generated automatically by MaxQuant, the user must select it in MaxQuant: Global Parameters –> tables

PlotAcquisitionCycle(MQCombined, 
                   palette = 'Set2',
                   plots_per_page = 6)
#> [[1]]

4.21 Post-translational modifications

The function PlotPTM(), takes as input the modificationSpecificPeptides.txt table and returns the main modifications found at the peptide level. The parameters can be adjusted to select the minimum number of peptides modified per group, and whether or not you would like to visualize the Unmodified peptides.

PlotPTM(MQCombined,
      peptides_modified = 1,
      plot_unmodified_peptides = FALSE,
      palette = 'Set2',
      aggregate_PTMs = TRUE,
      plots_per_page = 6)
#> [[1]]

4.22 Post-translational modifications across samples

The function PlotPTMAcrossSamples(), takes as input the modificationSpecificPeptides.txt table and one or multiple modification of interest. It returns a plot showing the intensities of that given PTM across the samples. It is important to specify correctly the name of the PTM, the same way as MaxQuant:


PlotPTMAcrossSamples(MQCombined, 
                     PTM_of_interest = c('Acetyl (Protein N-term)',
                                         'Oxidation (M)'),
                     long_names = TRUE,
                     sep_names = '_')
#> [[1]]

#> 
#> [[2]]

The parameters can be adjusted to select the minimum number of peptides modified per group, and whether or not you would like to visualize the Unmodified peptides.

5 Helper Functions

This package provides two extra functions to helps to analyze the proteomics data from MaxQuant:

The function make_MQCombined() takes as input the path to the combined folder resulting from MaxQuant analysis. It will read the tables needed and by default remove the potential contaminants, Reverse, and proteins identified only by site.

The function ReportTables() takes as input the path to the combined folder, and returns tables with information needed to create some of the most important plots in this package.


ReportTables(MQCombined,
           log_base = 2,
           intensity_type = 'Intensity')
#> $proteins
#>         Experiment Proteins Identified Missing values Potential contaminants
#> 1 Combined Samples                4751           <NA>                    100
#> 2      QC02_210326                4456            295                     91
#> 3      QC02_210331                4358            393                     92
#> 4      QC02_210402                4380            371                     94
#> 5      QC02_210406                4478            273                     92
#> 6      QC02_210410                4404            347                     94
#> 7      QC02_210411                4553            198                     93
#>   Reverse Only identified by site Peptide Sequences Identified
#> 1      66                      70                        47126
#> 2      43                      33                        29292
#> 3      38                      24                        30920
#> 4      42                      36                        27603
#> 5      49                      35                        30341
#> 6      47                      30                        31110
#> 7      47                      37                        32170
#>   Peptides/Proteins
#> 1               9.9
#> 2               6.6
#> 3               7.1
#> 4               6.3
#> 5               6.8
#> 6               7.1
#> 7               7.1
#> 
#> $intensities
#>    Experiment  mean    sd median   min   max    n
#> 1 QC02_210326 27.42 29.61  24.43 17.95 34.67 4456
#> 2 QC02_210331 27.67 29.84  24.65 18.15 34.79 4358
#> 3 QC02_210402 27.26 29.45  24.25 16.69 34.44 4380
#> 4 QC02_210406 28.49 30.68  25.50 17.83 35.71 4478
#> 5 QC02_210410 28.28 30.54  25.20 18.24 35.48 4404
#> 6 QC02_210411 28.42 30.65  25.36 16.75 35.68 4553
#> 
#> $charge
#>    Experiment   1    2    3   4   5   6
#> 1 QC02_210326 0.0 61.7 35.1 2.9 0.2 0.0
#> 2 QC02_210331 0.2 62.2 34.3 3.0 0.2 0.1
#> 3 QC02_210402 0.0 63.2 34.1 2.7 0.0 0.0
#> 4 QC02_210406 0.2 63.0 34.2 2.5 0.0 0.0
#> 5 QC02_210410 0.0 61.2 35.1 3.6 0.0 0.0
#> 6 QC02_210411 0.1 59.1 37.9 2.9 0.0 0.0
#> 
#> $GRAVY
#> # A tibble: 6 × 5
#>   Experiment  Mean  Max   Min   Median
#>   <chr>       <chr> <chr> <chr> <chr> 
#> 1 QC02_210326 -0.22 2.24  -2.69 -0.18 
#> 2 QC02_210331 -0.25 2.24  -2.69 -0.22 
#> 3 QC02_210402 -0.23 2.24  -2.69 -0.2  
#> 4 QC02_210406 -0.2  2.24  -2.69 -0.17 
#> 5 QC02_210410 -0.24 2.24  -2.69 -0.23 
#> 6 QC02_210411 -0.21 2.24  -2.69 -0.18 
#> 
#> $cleavages
#> # A tibble: 6 × 4
#> # Groups:   Experiment [6]
#>   Experiment       `0`   `1`   `2`
#>   <chr>          <dbl> <dbl> <dbl>
#> 1 " QC02_210326"  2300   304    14
#> 2 " QC02_210331"  2153   282    10
#> 3 " QC02_210402"  2151   265     8
#> 4 " QC02_210406"  2355   309    15
#> 5 " QC02_210410"  2203   324    12
#> 6 " QC02_210411"  2489   330    17
#> 
#> $overlap
#> # A tibble: 6 × 2
#>   samples  Freq
#>     <dbl> <int>
#> 1       1    47
#> 2       2    55
#> 3       3    97
#> 4       4   141
#> 5       5   308
#> 6       6  3890

6 Session Info

sessionInfo()
#> R version 4.3.0 RC (2023-04-13 r84269)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.2 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] MQmetrics_1.8.0  BiocStyle_2.28.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.3        xfun_0.39           bslib_0.4.2        
#>  [4] ggplot2_3.4.2       ggrepel_0.9.3       rstatix_0.7.2      
#>  [7] lattice_0.21-8      tzdb_0.3.0          vctrs_0.6.2        
#> [10] tools_4.3.0         generics_0.1.3      parallel_4.3.0     
#> [13] tibble_3.2.1        fansi_1.0.4         highr_0.10         
#> [16] pkgconfig_2.0.3     Matrix_1.5-4        RColorBrewer_1.1-3 
#> [19] lifecycle_1.0.3     compiler_4.3.0      farver_2.1.1       
#> [22] stringr_1.5.0       munsell_0.5.0       ggforce_0.4.1      
#> [25] carData_3.0-5       htmltools_0.5.5     sass_0.4.5         
#> [28] yaml_2.3.7          crayon_1.5.2        pillar_1.9.0       
#> [31] car_3.1-2           ggpubr_0.6.0        jquerylib_0.1.4    
#> [34] tidyr_1.3.0         MASS_7.3-59         cachem_1.0.7       
#> [37] magick_2.7.4        abind_1.4-5         nlme_3.1-162       
#> [40] tidyselect_1.2.0    digest_0.6.31       stringi_1.7.12     
#> [43] dplyr_1.1.2         reshape2_1.4.4      purrr_1.0.1        
#> [46] bookdown_0.33       splines_4.3.0       labeling_0.4.2     
#> [49] cowplot_1.1.1       polyclip_1.10-4     fastmap_1.1.1      
#> [52] grid_4.3.0          archive_1.1.5       colorspace_2.1-0   
#> [55] cli_3.6.1           magrittr_2.0.3      utf8_1.2.3         
#> [58] broom_1.0.4         readr_2.1.4         withr_2.5.0        
#> [61] scales_1.2.1        backports_1.4.1     bit64_4.0.5        
#> [64] rmarkdown_2.21      bit_4.0.5           gridExtra_2.3      
#> [67] ggsignif_0.6.4      hms_1.1.3           evaluate_0.20      
#> [70] knitr_1.42          gghalves_0.1.4      mgcv_1.8-42        
#> [73] rlang_1.1.0         Rcpp_1.0.10         polynom_1.4-1      
#> [76] glue_1.6.2          tweenr_2.0.2        BiocManager_1.30.20
#> [79] vroom_1.6.1         jsonlite_1.8.4      R6_2.5.1           
#> [82] plyr_1.8.8