ludgergoeminne/MSqRob: Robust statistical inference for quantitative LC-MS proteomics

The MSqRob package allows a user to do quantitative protein-level statistical inference on LC-MS proteomics data. More specifically, our package makes use of peptide-level input data, thus correcting for unbalancedness and peptide-specific biases. As previously shown (Goeminne et al. (2015)), this approach is both more sensitive and specific than summarizing peptide- level input to protein-level values. Model estimates are stabilized by ridge regression, empirical Bayes variance estimation and downweighing of outliers. Currently, only label-free proteomics data types are supported. The MSqRob Shiny App allows for an easy-to-use graphical user interface that requires no programming skills. Interactive plots are outputted and results are automatically exported to Excel. The authors kindly ask to make a reference to (Goeminne et al. (2016) and Goeminne et al. (2017)) when making use of this package in any kind of publication or presentation. Ludger Goeminne, Andrea Argentini, Lennart Martens and Lieven Clement (2015). Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines. Journal of Proteome Research 15(10), 3550-3562. When using MSqRob, please refer to our published algorithm: Goeminne, L. J. E., Gevaert, K., and 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), pp 657-668. When using the MSqRob App, please refer to: Goeminne, L. J. E., Gevaert, K. and Clement, L. (2017). Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob. Journal of Proteomics (in press).

install.packages("remotes")
remotes::install_github("ludgergoeminne/MSqRob")