MQmetrics

MQmetrics serves as a tool to analyze the quality of the proteomics data coming from the LC-MS/MS. As input, it takes the directory to the files resulting from a MaxQuant analysis and returns a pdf with diverse parameters.

Installation

You can install the stable version of MQmetrics from Biocodunctor with:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")


BiocManager::install("MQmetrics")

I highly advice to install the development version from bioconductor since it contains several updates (check the news).

BiocManager::install("MQmetrics",  version = 'devel')

You can also install the latest version from GitHub if you know what you are doing with:

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

Example

MQmetrics it’s easy to use, and will provide you information about your Proteomics MaxQuant analysis.

Start by loading the library. By default, the MaxQuant results will be stored in a folder named Combined. The directory to that folder is all you need to use MQmetrics.

library(MQmetrics)

MQPathCombined <- "D:/Documents/MaxQuant_results/example5/combined/"
# Use forward slashes in both windows/or linux.
MQPathCombined <- "/home/alvaro/Documents/MaxQuant/example5/combined/"

The main function of the package is generateReport(). It will generate a PDF report containing several visualizations and tables from different MaxQuant output tables. As input it is only necessary to provide the Path to the Combined folder of MaxQuant output.

generateReport(MQPathCombined, long_names = TRUE, sep_names = '_')

Two useful parameters of every function including generateReport() are: long_names and sep_names. They will allow a clear visualization of those samples that have long names separated by a character. In this example, the Experiment names are one full string separated by underscores (_): PLK010_QC02_210121_HeLa_125ng_150meth.

# make_MQCombined reads all the files needed from the MaxQuant output and
# remove Potential contaminants, reverse, and proteins identified by site only.

MQCombined <- make_MQCombined(MQPathCombined, remove_contaminants = TRUE) 

PlotIntensity(MQCombined, long_names = TRUE, sep_names = '_')

Check the vignettes for more information.