MQmetrics

Alvaro Sanchez-Villalba

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, 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.

Workflow

Install the package

You can install MQmetrics from Biocodunctor with:

You can install the development version from GitHub with:

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/'

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:

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.

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’.

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.

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.

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.

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.

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.