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.
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:
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.
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) MaxQuant Analysis Parameters
MaxQuantAnalysisInfo(MQCombined)
[1] "The MaxQuant output directory is: /tmp/RtmpL2BF5S/Rinst963573033bef8/MQmetrics/extdata/combined/"
[1] "The experiment started the day: 16/04/2021 at the time: 18:07:23."
[1] "The whole experiment lasted: 02:15 (hours:minutes)."
[1] "The MaxQuant version used was: 1.6.17.0"
[1] "The user was: thomas.stehrer"
[1] "The machine name was: FGU045PC004"
[1] "The PSM FDR was: 0.01"
[1] "The protein FDR was: 0.01"
[1] "The match between runs was: "
[1] "The fasta file used was: C:\\MaxQuant_Databases\\iRT_peptides_Biognosys_irtfusion.fasta;C:\\MaxQuant_Databases\\UP000005640_9606.fasta"
[1] "The iBAQ presence is: False"
[1] "The PTM selected is/are: Oxidation (M);Acetyl (Protein N-term)"Visualizations.
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')
The function PlotPeptidesIdentified(), will take as input the summary table and show the number of peptides sequences identified in each sample.
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.
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')
The function PlotPeaks(), will take as input the summary.txt table and show the number peaks detected and sequenced in each sample.
The function PlotIsotopePattern(),will take as input the summary.txt table and show the number isotope patterns detected and sequenced in each sample.
The function PlotCharge(), will take as input the evidence.txt table and show the charge-state of the precursor ion in each sample.
The function PlotProteaseSpecificity(), will take as input the summary.txt table and show the number peaks detected and sequenced in each sample.
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)
The function PlotAndromedaScore(), takes as input the peptides.txt table and returns the distribution of MaxQuant’s Andromeda Score.
The function PlotIdentificationType(), takes as input the peptides.txt and proteinGroups.txt tables and returns the number of peptides and proteins identified by Matching Between Runs or by MS/MS.
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')
The function PlotPCA() takes as input the proteinGroups.txt table and creates a Principal Componente Analysis plot of each Experiment.
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.
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.
The function PlotProteinOverlap() takes as input the proteinGroups.txt table and returns a plot that shows the number of proteins identified in the samples.
The function PlotProteinCoverage() takes as input the peptides.txt and proteinGroups.txt tables and a protein Uniprot ID. It shows, if present, the coverage of that protein in each of the samples.
PlotProteinCoverage(MQCombined,
UniprotID = "P55072",
log_base = 2,
segment_width = 1,
palette = 'Set2',
plots_per_page = 6)
The function PlotiRT() takes as input the evidence.txt table and returns, if found the iRT peptides from Biognosys. Their retention time and intensity.
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.
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.
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.
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 minimun 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',
plots_per_page = 6)
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 x 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 x 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 x 2
#> samples Freq
#> <dbl> <int>
#> 1 1 47
#> 2 2 55
#> 3 3 97
#> 4 4 141
#> 5 5 308
#> 6 6 3890Session Info
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
#>
#> 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
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MQmetrics_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] sass_0.4.0 tidyr_1.1.3 jsonlite_1.7.2 splines_4.1.0
#> [5] carData_3.0-4 bslib_0.2.5.1 assertthat_0.2.1 highr_0.9
#> [9] cellranger_1.1.0 yaml_2.2.1 pillar_1.6.1 backports_1.2.1
#> [13] lattice_0.20-44 glue_1.4.2 chron_2.3-56 digest_0.6.27
#> [17] RColorBrewer_1.1-2 ggsignif_0.6.1 polyclip_1.10-0 colorspace_2.0-1
#> [21] Matrix_1.3-3 cowplot_1.1.1 htmltools_0.5.1.1 plyr_1.8.6
#> [25] pkgconfig_2.0.3 broom_0.7.6 haven_2.4.1 purrr_0.3.4
#> [29] scales_1.1.1 tweenr_1.0.2 openxlsx_4.2.3 rio_0.5.26
#> [33] ggforce_0.3.3 tibble_3.1.2 mgcv_1.8-35 generics_0.1.0
#> [37] farver_2.1.0 car_3.0-10 ggplot2_3.3.3 ellipsis_0.3.2
#> [41] ggpubr_0.4.0 cli_2.5.0 magrittr_2.0.1 crayon_1.4.1
#> [45] readxl_1.3.1 evaluate_0.14 ps_1.6.0 fansi_0.4.2
#> [49] nlme_3.1-152 MASS_7.3-54 rstatix_0.7.0 forcats_0.5.1
#> [53] foreign_0.8-81 tools_4.1.0 data.table_1.14.0 hms_1.1.0
#> [57] lifecycle_1.0.0 stringr_1.4.0 munsell_0.5.0 zip_2.1.1
#> [61] compiler_4.1.0 jquerylib_0.1.4 rlang_0.4.11 grid_4.1.0
#> [65] ggridges_0.5.3 rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.8
#> [69] gtable_0.3.0 abind_1.4-5 DBI_1.1.1 curl_4.3.1
#> [73] polynom_1.4-0 reshape2_1.4.4 R6_2.5.0 gridExtra_2.3
#> [77] knitr_1.33 dplyr_1.0.6 utf8_1.2.1 readr_1.4.0
#> [81] stringi_1.6.2 Rcpp_1.0.6 vctrs_0.3.8 tidyselect_1.1.1
#> [85] xfun_0.23