Tools for protein significance analysis in DDA,SRM and DIA proteomic experiments for label-free workflows or workflows with stable isotope labeled reference
MSstats, an R package in Bioconductor, supports protein significance analysis for statistical relative quantification of proteins and peptides in global, targeted and data-independent proteomics. It handles shotgun, label-free and label-based (universal synthetic peptide-based) SRM (selected reaction monitoring), and SWATH/DIA (data independent acquisition) experiments. It can be used for experiments with complex designs (e.g. comparing more than two experimental conditions, or a time course). MSstats provides functionalities for three types of analysis: 1) Data processing and visualization 2) Model-based statistical analysis, in particular testing for differential protein abundance between condition and estimation of protein abundance in individual biological samples or conditions on a relative scale 3) Model-based calculation of a sample size for a future experiment, while using the current dataset as a pilot study for variance estimation. The statistical analysis is based on a family of linear mixed-effects models.
User manual for MSstats (A new option for feature selection is updated in the manual)
Information about the most recent MSstats in Bioconductor
Tutorial for MSstats as external tool in Skyline
R script for example data in MSstats
Known issues and proposed solutions
From Bioconductor: MSstats
MSstats 3.18.1 (Bioconductor version : Release 3.10, R version >= 3.6)
Type the following in R console window
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("MSstats") library(MSstats)
From GitHub: MSstats
MSstats Bioconductor development version : link
The development version of the package MSstats is the most recent and is available here. The versioning of the main package is updated twice a year, to synchronise with the Bioconductor release.
For use via Skyline external tool
More datasets including data, R script and output are available in MSstats material github.
- Meena Choi, Northeastern University
- Tsung-Heng Tsai, Northeastern University
- DOI : 10.18129/B9.bioc.MSstats
- M. Choi et al. “MSstats: an R package for statistical analysis for quantitative mass spectrometry-based proteomic experiments.” Bioinformatics (2014), 30 (17): 2524-2526
List of citations
MSstats has been cited the the following manuscripts. Link