Below users can find a series of vignettes, tutorials, and relevant publications to help get started with MSstats.

Introduction

MSstats is an open-source R package for statistical relative quantification of proteins and peptides in mass-spectrometry-based proteomics. MSstats supports label-free and label-based experimental workflows and data-dependent, targeted and data-independent spectral acquisition. It operates by taking in spectral peaks that have been identified and quantified, and then generates either a list of peptides or proteins with varying levels of abundance or concise summaries detailing the relative abundance of peptides or proteins. The underlying mechanism of MSstats is based on a versatile set of linear mixed models.

Introductory Vignettes

Introduction Vignette
MSstats User Manual
Skyline Tutorial

We encourage new users to check out recordings from our May Institute course on YouTube. Topics covered range from an R crash course for beginners to more advanced statistical modeling for mass spectrometry data.

Video tutorials from May Institute

Publications

  1. T. Huang et al. “Statistical detection of differentially abundant proteins in experiments with repeated measures designs and isobaric labeling”. Journal of Proteome Research, 22:2641, 2023. [LINK]
  2. D. Kohler et al. “MSstats version 4.0: statistical analyses of quantitative mass spectrometry-based proteomic experiments with chromatography-based quantification at scale”. Journal of Proteome Research, 22:1466, 2023. [LINK]
  3. L. Malinovska et al. “Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications”, Nature Protocols, 18:659, 2023. [LINK]
  4. D. Kohler et al. “MSstatsShiny: a GUI for versatile, scalable, and reproducible statistical analyses of quantitative proteomic experiments”, Journal of Proteome Research, 22:551, 2023. [LINK]
  5. D. Kohler et al. “MSstatsPTM: Statistical relative quantification of posttranslational modifications in bottom-up mass spectrometry-based proteomics”, Molecular & Cellular Proteomics, 22:100477, 2022. [LINK]
  6. M. Choi et al. “MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets”, Nature Methods, 17:981, 2020. [LINK]
  7. T. Huang et al. “MSstatsTMT: Statistical detection of differentially abundant proteins in experiments with isobaric labeling and multiple mixtures”, Molecular & Cellular Proteomics, mcp.RA120.002105, 2020. [LINK]
  8. T.-H. Tsai et al. “Selection of features with consistent profiles improves relative protein quantification in mass spectrometry experiments”. Molecular & Cellular Proteomics, mcp.RA119.001792, 2020. [LINK]
  9. E. Dogu et al. “MSstatsQC 2.0: R/Bioconductor package for statistical quality control of mass spectrometry-based proteomic experiments”. Journal of Proteome Research, 18:678, 2019. [LINK]
  10. C. Galitzine et al. “Nonlinear regression improves accuracy of characterization of multiplexed mass spectrometric assays”, Molecular & Cellular Proteomics, RA117.000322, 2018. [LINK]
  11. T.-H. Tsai et al. “Statistical characterization of therapeutic protein modifications”. Scientific Reports, 7, 7896, 2017. [LINK]
  12. A. L. Oberg, O. Vitek. “Statistical design of quantitative mass spectrometry-based proteomic experiments”. Journal of Proteome Research, 8:2144, 2009 [LINK].

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