The repository provides the source code for generating the book:
"Statistical analysis of mass spectrometry-based proteomics data".
The book is focused around the msqrob2 software.
The sticker is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The book is compiled using quarto:
- Install all dependencies.
installed.packages(c("BiocManager", "remotes"))
BiocManager::install(version = "devel")
remotes::install_deps(dependencies = TRUE, repos = BiocManager::repositories())- Run quarto (note that
freeze:autois enabled meaning that quarto will render only the chapters that have changed since last compilation)
quarto render
This material is licensed under a Creative Commons
Attribution-ShareAlike 4.0 International License. You are free to
share (copy and redistribute the material in any medium or format)
and adapt (remix, transform, and build upon the material) for any
purpose, even commercially, as long as you give appropriate credit and
distribute your contributions under the same license as the original.
Please cite this book as:
TODO: add citation once published
Please cite the msqrob2 package as:
Goeminne L, Gevaert K, Clement L (2016). "Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics." Molecular & Cellular Proteomics, 15(2), 657-668. doi:10.1074/mcp.m115.055897.
If you opt for a summarisation-based workflow, you can also cite:
Sticker A, Goeminne L, Martens L, Clement L (2020). "Robust Summarization and Inference in Proteome-wide Label-free Quantification." Molecular & Cellular Proteomics, 19(7), 1209-1219. doi:10.1074/mcp.ra119.001624.
If you use TMT-based workflows, please cite
Vandenbulcke S, Vanderaa C, Crook O, Martens L, Clement L. Msqrob2TMT: Robust linear mixed models for inferring differential abundant proteins in labeled experiments with arbitrarily complex design. Mol Cell Proteomics. 2025;24(7):101002.