Proteus: an R package for downstream analysis of MaxQuant output
Marek GierlińskiiD, Francesco Gastaldello, Christian ColeiD, Geoffrey J. BartoniD
Abstract
Proteus is a package for downstream analysis of MaxQuant evidence data in the R environment. It provides tools for peptide and protein aggregation, quality checks, data exploration and visualisation. Interactive analysis is implemented in the Shiny framework, where individual peptides or protein may be examined in the context of a volcano plot. Proteus performs differential expression analysis with the well-established tool limma, which offers robust treatment of missing data, frequently encountered in label-free mass-spectrometry experiments. We demonstrate on real and simulated data that limma results in improved sensitivity over random imputation combined with a t-test as implemented in the popular package Perseus. Embedding Proteus in R provides access to a wide selection of statistical and graphical tools for further analysis and reproducibility by scripting. Availability and implementation: The open-source R package, including example data and tutorials, is available to install from GitHub (https://github.com/bartongroup/proteus).
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 48
- Assay
- Research Focus
- Approach
Abstract summary
Claude Opus 5.5 · xhigh effort · 2 Nov 2026 · from the abstract · CC BY-NC-ND 4.0
Proteus is an open-source R package with Shiny interactive visualization that aggregates MaxQuant proteomics evidence data and uses limma to improve differential expression sensitivity over Perseus random imputation.
Label-free mass spectrometry proteomics experiments frequently contain missing data, and standard workflows such as Perseus rely on random imputation paired with a t-test. Proteus processes MaxQuant evidence data in R using limma for differential expression, improving sensitivity without random imputation while supporting interactive Shiny exploration and scripted reproducibility.
Key results
ShownDifferential expression analysis in Proteus using limma achieves higher sensitivity in label-free mass spectrometry data than random imputation followed by a t-test in Perseus.
Evaluations on both real and simulated mass spectrometry datasets demonstrated that limma handled missing data robustly and improved sensitivity compared with the random imputation and t-test workflow implemented in Perseus. · Abstract
Methods, data and reproducibility
Built an R package that ingests MaxQuant evidence files to perform peptide-to-protein aggregation, quality control, limma-based differential expression analysis, and interactive volcano plot inspection via Shiny.
- Real and simulated label-free mass spectrometry proteomics datasets processed with MaxQuant
- Methods & tools
- Proteus, MaxQuant, R, Shiny, limma, Perseus
- Data & accessions
- https://github.com/bartongroup/proteus
- Software & environment
- R environment
- Reproducibility gaps
- Specific R package versions, quantitative sensitivity benchmarks, and accessions for the real proteomics dataset are not reported in the abstract.
Limitations (1)
- The abstract does not report quantitative sensitivity metrics, false discovery rates, or sample sizes for the real and simulated benchmarks. · noted in this summary
Who should read:Read this if you analyze MaxQuant label-free proteomics output in R and want limma-based differential expression and interactive Shiny visualization.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- prolfqua: A Comprehensive R-package for Proteomics Differential Expression Analysis
Witold Wolski, Paolo Nanni, … Christian Panse·Journal of Proteome ResearchPeer-reviewed·Mar 20, 2023
- einprot: flexible, easy-to-use, reproducible workflows for statistical analysis of quantitative proteomics data
Charlotte Soneson, Vytautas Iešmantavičius, … Jan Seebacher·bioRxiv·Jul 27, 2023
- Autoprot: Processing, Analysis and Visualization of Proteomics Data in Python
Julian Bender, Wignand W. D. Mühlhäuser, … Bettina Warscheid·bioRxiv·Jan 18, 2024
- AlphaPeptStats: an open-source Python package for automated, scalable and industrial-strength statistical analysis of mass spectrometry-based proteomics
E. Krismer, M. T. Strauss, M. Mann·bioRxiv·Mar 10, 2023
- Analysis and visualization of quantitative proteomics data using FragPipe-Analyst
Yi Hsiao, Haijian Zhang, … Alexey I. Nesvizhskii·Journal of Proteome ResearchPeer-reviewed·Sep 10, 2024
- ProteoPlotter: an executable proteomics visualization tool compatible with Perseus
Esther Olabisi-Adeniyi, Jason A. McAlister, … Jennifer Geddes‐McAlister·Journal of Proteome ResearchPeer-reviewed·May 13, 2025