Course · MSstats

Statistics for mass spectrometry with MSstats

Days 1–3 · November 2–4, 2026 · 10:00 a.m.–12:30 p.m. EST. Led by Devon Kohler, Sarah Szvetecz, and Tony Wu.

Background

MSstats sessions cover the statistical analysis of quantitative mass spectrometry–based proteomic experiments using MSstats. Topics include normalization, missing value imputation, summarization of protein abundances from multiple spectral features, derivation of confidence intervals for fold changes, testing proteins for differential abundance, and multivariate analysis for the discovery of biomarkers. Sessions combine lectures with hands-on analysis of case studies, working in the MSstatsShiny GUI.

Who it’s for

Experimental and computational scientists with experience in proteomics. Some prior experience with statistics and proteomics is assumed.

Schedule

Three online sessions, each 10:00 a.m.–12:30 p.m. EST (7:00–9:30 a.m. PST · 4:00–6:30 p.m. CET). Every day follows the same format: two one-hour lectures with a short break, followed by general discussion. Lecture titles will be added as the program is finalized.

Day 1 · Monday, Nov 2
  • 10:00–11:00 Lecture 1 — to be announced
  • 11:00–11:15 Break and questions
  • 11:15–12:15 Lecture 2 — to be announced
  • 12:15–12:30 General discussion
Day 2 · Tuesday, Nov 3
  • 10:00–11:00 Lecture 1 — to be announced
  • 11:00–11:15 Break and questions
  • 11:15–12:15 Lecture 2 — to be announced
  • 12:15–12:30 General discussion
Day 3 · Wednesday, Nov 4
  • 10:00–11:00 Lecture 1 — to be announced
  • 11:00–11:15 Break and questions
  • 11:15–12:15 Lecture 2 — to be announced
  • 12:15–12:30 General discussion

Software and installation

To follow the hands-on sessions, please install the MSstats family of packages ahead of time. A modern laptop that can open downloaded files is all that is required.

  1. Install R (≥ 4.5) from CRAN. If R is already installed, please make sure it is up to date.
  2. On Windows, also install Rtools (matched to your R version).
  3. Install RStudio Desktop (recommended).
  4. Open R or RStudio and run the installation code below in the console.

Install MSstatsShiny (and the MSstats packages it depends on) from Bioconductor:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MSstatsShiny")

If you upgraded your version of R, be sure to re-run the installation code so the packages are rebuilt for the new version.

If you have trouble installing from Bioconductor, you can install the development version from GitHub instead:

install.packages("remotes")
remotes::install_github("Vitek-Lab/MSstatsShiny", dependencies = TRUE)

If you see an error when running the groupComparison function, reinstall lme4 from source:

install.packages("lme4", type = "source")

Launch the MSstatsShiny app to confirm your installation works:

MSstatsShiny::launch_MSstatsShiny()

Software tools used in this course are available under permissive open-source licenses. Note that some knowledge sources such as pathway databases may require licenses for use outside this course in a commercial setting.

Materials

Presentation slides, code, and datasets are shared with registered participants ahead of sessions.

Links and resources

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