
QFeaturesGUI is a collection of Shiny applications that provide graphical
user interfaces for MS-based proteomics data analysis using the
R for Mass Spectrometry/Bioconductor
ecosystem. It supports both bulk and single-cell proteomics (SCP)
workflows and builds on the
QFeatures and
scp packages.
Rather than a single application, QFeaturesGUI is composed of multiple apps, each dedicated to specific steps of the proteomics data analysis workflow.
QFeatures and scp
Currently available applications include:
Data import (import)
Import quantitative proteomics data into QFeatures objects
Data processing (process)
Perform common data processing steps on QFeatures and scp objects
Data visualisation (visualise)
Explore and visualise QFeatures objects
Additional applications will be added in future releases.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("rformassspectrometry/QFeaturesGUI")
QFeaturesGUI builds upon and integrates with several Bioconductor packages
and projects for MS-based proteomics and single-cell data analysis:
QFeatures
Data infrastructure for MS-based quantitative proteomics.
scp
Extension of QFeatures for single-cell proteomics data analysis.
msqrob2
Robust statistical inference for quantitative LC-MS proteomics
RforMassSpectrometry
A collection of R packages for the analysis and interpretation of mass
spectrometry data.
SingleCellExperiment
Core Bioconductor data container for single-cell data.
SummarizedExperiment
Provides a general container for high-throughput assays and associated metadata.
MultiAssayExperiment
Infrastructure for managing and integrating multiple assays for the same set of samples.
Contributions to QFeaturesGUI are welcome. These include bug reports,
feature requests, and suggestions for new applications or improvements to
existing ones. Contributors agree to abide by the terms of the project
Code of Conduct.
Issues and enhancement requests can be submitted via the GitHub issue tracker.
When reporting bugs, please include a minimal reproducible example when possible, along with information about your R session and package versions.