Quality Control Dashboard for MaxQuant Proteomics Data

The Quality Control Dashboard for MaxQuant Proteomics Data is a R Shiny application, which takes the output text files (proteinGroups.txt, peptides.txt, msms.txt, summary.txt) from MaxQuant and a custom text file with the design of experiment information (DOE) for QC steps before further downstream analysis.

Input files

From MaxQuant output:

The files are located in the ~/combined/txt directory.

proteinGroups.txt (required)
peptides.txt (optional)
msms.txt (optional)
summary.txt (optional)

Prepared by the user:

DOE (ex: doe.txt, optional but recommended)

The following 4 columns are required:
- Sample.id
- Run.order
- Type
- Sample.type
Sample.id should match the data in MaxQuant.
Avoid naming the sample (Sample.id) starting with a number.
Only 'control' and 'sample' are admitted in the "Type" column.

Run QC-MQ

Method 1: Use QC-MQ online

QC-MQ is internally deployed at https://report.pri.bms.com/QC-MQ/ for online use. Have the input files ready and follow the instructions from the dashboard to upload the files and at least input the prefix to run the QC.

Method 2: Launch QC-MQ from R

Step 1: Install R and RStudio

Please check CRAN (https://cran.r-project.org/) for the installation of R.
Please check https://www.rstudio.com/ for the installation of RStudio.

Step 2: Install R Shiny package and other packages required by QC-MQ

Once the R and RStudio are installed, start an R session using RStudio and run the following lines:

load.lib <- c("shiny", "rmarkdown", "BiocManager", "shinyBS", "shinydashboard", "shinyFiles", "colorspace", "tools", "shinyjs", "pvca", "Biobase", "ggthemes", "ggplot2", "scales", "shinyWidgets", "shinyalert", "reshape2", "stringr", "quantro", "plyr", "tidyr", "plotly", "dplyr")
install.lib <- load.lib[!load.lib %in% installed.packages()]
for(lib in install.lib) install.packages(lib, dependencies=TRUE)
sapply(load.lib, require, character=TRUE)

Step 3: Download the source code to the working directory

The zipped QC-MQ source code can be downloaded by clicking the clone or download button followed by clicking the Download Zip button from the page of this repository.
Move and unzip the downloaded file to the working directory.

Step 4: Start the app

In the RStudio console, set the working directory at where the downloaded QC-MQ code is located by:

setwd("~/path/workingdirectory")  # You have to put your own path instead of "~/path/workingdirectory"

Launch the app by:

library(shiny)
library(shinydashboard)
runApp(appDir = getwd())

Step 5: Run the QC

Have the input files ready and follow the instructions from the dashboard to upload files and input prefix to run the QC for MaxQuant processed data

Example files from MaxQuant output

Three different example datasets are included in the QC-MQ/example_file_MQoutput folder. Users can upload the example files and set the prefix to test the app. (note: msms.txt files are not included in this repository due to the size limitation, QC-MQ allows user running QC without uploading the msms.txt.)

sylviachien/QC-MQ