MSstats · Day 3 of 3

Interpretation with MSstatsBioNet and INDRA

Wednesday, November 4, 2026 · 10:00 a.m.–12:30 p.m. EST. Led by Tony Wu, Benjamin Gyori, and Swaraj Patil.

This session

Interpreting proteomics data using biological networks representing cellular mechanisms is a powerful approach to gaining actionable insights. In this session we first introduce the INDRA system, developed by the Gyori Lab, which automatically assembles mechanisms into networks from both pathway databases and text-mined biomedical literature. We then dive deeper into the INDRA Database, the INDRA Network Search tool, and the INDRA Biomedical Discovery Engine which each facilitate gaining insights from data and generating hypotheses based on large-scale networks. Finally, we introduce MSstatsBioNet, which queries INDRA to construct an experiment-specific biological subnetwork from statistical results of MS proteomics experiments, facilitating biological interpretation. The session will be conducted using web-based UIs and RShiny GUIs.

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 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

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

MSstats packages

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()

INDRA

The INDRA portions of this session are run through web-based interfaces, so no local installation is required. Please have a current web browser available, and register an account on the INDRA Biomedical Discovery Engine ahead of the session.

If you would like to run INDRA yourself, it is a Python package. It is tested on Python 3.8–3.14; other versions generally work but are not tested. The preferred installation points pip at the source repository:

pip install git+https://github.com/gyorilab/indra.git

Releases are also on PyPI, though they usually lag behind the repository:

pip install indra

Individual INDRA modules need additional “extra” dependencies, which are listed in the INDRA documentation. Installing INDRA is optional for this session.

A prebuilt image is available if you prefer to run INDRA in Docker:

docker pull labsyspharm/indra

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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