GUI implementation for the msqrob2 package

msqrob2 is a Bioconductor package for differentially expression analysis in mass spectrometry using the QFeatures class infrastructure.

The msqrob2gui package implements a graphical user interface by means of a shiny app, alleviating the need for programming skills to use msqrob2.

Installation

Get the remotes packages to install from GitHub

# Check if remotes is installed. Otherwise install it.
if (!require("remotes", quietly = TRUE)){
    install.packages("remotes")
}

Install the msqrob2gui package from the statOmics branch.

remotes::install_github("statOmics/msqrob2gui")

Load the package.

library(msqrob2gui)

Launch app

The msqrob2gui package provides three gui's:

1. A basic App for data processing: launchMsqrob2DataProcessingApp() that starts from the raw search results and provides filtering, log-transformation, normalisation, summarisation upto protein level abundances. The output is a QFeatures object. For more advanced options or to develop your own data processing workflow we recommend the use of QFeaturesGUI.

2. launchMsqrob2ModelingApp() or launchMsqrob2ModellingApp(): The main msqrob2 modeling workhorse. It takes in a QFeatures object, e.g. generated with the DataProcessingApp or QFeaturesGUI and provides data exploration, statistical modeling and inference to prioritise differential abundant features, and exploring the results with the altSEE extension for iSEE. The altSEE functionality enables the user to interactively explore the DA proteins along with the raw and processed precursor/PSM-level and/or peptide-level quantificiations.

3. launchMsqrob2App(): An end-to-end workflow integrating 1 and 2 in one App.

4. launchMsqrob2ExploreApp() the stand-alone altSEE/iSEE app for exploring QFeatures objects.

Note, that all apps provide the user with R-objects, plots and RMD reports that can be downloaded to document the data analysis in reproducible way.

library(msqrob2gui)

# Data Processing only (Alternatively users can use QFeaturesGUI)
launchMsqrob2DataProcessingApp()

# Statistical Modeling only, starting from processed data stored as a QFeatures 
# object followed with altSEE interactive visualisation
# 
launchMsqrob2ModelingApp()

# End-to-end workflow with Data Processing, Statistical Modeling and altSEE 
# interactive visualisation
launchMsqrob2App()

# Interactive altSEE visualisation of QFeatures objects
launchMsqrob2ExploreApp()