MSstats · Day 2 of 3

PTMs & Chemoproteomics

Tuesday, November 3, 2026 · 10:00 a.m.–12:30 p.m. EST. Led by Devon Kohler, Swaraj Patil, Sarah Szvetecz, and Tony Wu.

This session

Post-translational modification (PTM) and chemoproteomics experiments provide important insights into protein regulation and drug‐protein interactions, but introduce unique challenges for statistical analysis. PTM experiments often have limited measurements at individual modification sites and require distinguishing changes in PTM abundance from changes in overall protein abundance, while chemoproteomics experiments measure protein responses across multiple drug concentrations and may not follow a standard dose-response curve shape. In the first part of the session, we introduce MSstatsPTM, a tool for detecting differential PTM abundance while accounting for changes in overall protein abundance. The second part focuses on statistical modeling for chemoproteomics experiments with MSstatsResponse, which applies flexible dose-response modeling to protein‐ or PTM‐level data to reliably detect drug‐protein interactions, estimate IC50 values, and visualize response curves. Through hands-on examples in our RShiny GUIs, participants will gain practical skills for analyzing both experiment types.

Session schedule

One online session, 10:00 a.m.–12:30 p.m. EST (7:00–9:30 a.m. PST · 4:00–6:30 p.m. CET): two one-hour lectures with a short break, followed by 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

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.

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

All days in this course

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