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.


MSstats_v4.6.3_manual (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 4.2.0 (Bioconductor version : Release 3.14, R version >= 4.1)

Type the following in R console window

if (!requireNamespace("BiocManager", quietly = TRUE))

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 synchronize with the Bioconductor release.

For use via Skyline external tool

To use MSstats via a graphical user interface, as an external tool in Skyline, please see the info here. (known issues and proposed solutions)

More datasets including data, R script and output are available in MSstats material github.


  • Meena Choi,  Genentech
  • Mateusz Staniak,  University of Wrocław

Citing MSstats

List of citations

MSstats has been cited the the following manuscripts. Link