Cardinal · Day 2 of 2

Case studies on statistics and machine learning with MSI data

Friday, November 6, 2026 · 10:00 a.m.–12:30 p.m. EST. Led by Kylie Bemis, Sai Srikanth Lakkimsetty, Ethan Rogers, and Yinyue Zhu.

This session

This session will cover analysis of preprocessed MSI data. First we will cover region of interest (ROI) discovery using Cardinal’s univariate and multivariate segmentation methods. We will then conclude with a case study of differential abundance between ROIs and experimental conditions.

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 · Friday, Nov 6
  • 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

This course covers the basics of data manipulation, visualization, and statistical analysis of mass spectrometry imaging (MSI) experiments using Cardinal and R. We begin by introducing the fundamental ways of interacting with MSI datasets and experimental metadata in Cardinal, followed by preprocessing and visualization of mass spectra and ion images. We then demonstrate the application and interpretation of statistical and machine learning methods for segmentation, classification, and hypothesis testing. Sessions mix lectures with code demonstrations using datasets from public repositories such as PRIDE and MassIVE.

Who it’s for

Biologists, chemists, bioinformaticians, computer scientists, data scientists, statisticians, and engineers who are comfortable with the basics of R and interested in working with MSI data in Cardinal.

Software and installation

If you would like to follow along with the code demonstrations, please install the software below ahead of time. We will use recent versions of R and Bioconductor:

Please install or update to these versions if you wish to follow along with the examples.

  1. Install R (≥ 4.6) from CRAN, and (recommended) RStudio Desktop. On Windows, also install Rtools.
  2. Open R or RStudio and run the installation code below in the console.
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("Cardinal", "CardinalWorkflows"))

Materials

Presentation slides, code, and datasets are shared with registered participants ahead of each session, and recordings are published afterward.

Links and resources

All days in this course

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