Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.


  • May 1-3, 2019 — We will teach a Cardinal workshop as part of the May Institute short course series in computation and statistics for proteomics
    • See the May Institute course website for more details
    • Workshop will include an introductory lecture to basic Cardinal concepts, followed by a hands-on tutorial of common statistical analyses
  • October 30, 2018Cardinal 2.0 is available on Bioconductor 3.8! This is a major update with many new features including:
    • New MS imaging data classes with improved support for working with large experiments
    • Rewritten visualization methods with support for non-gridded pixel coordinates and new color schemes
    • Full read/write support for both imzML ‘continuous’ and ‘processed’ formats
    • New preprocessing workflows with support for queueing of delayed processing steps and out-of-memory parallel execution
  • May 4, 2016Cardinal 1.4 is available on Bioconductor 3.3
  • December 16, 2015Cardinal 1.3 is available on Github with *experimental* support for:
    • Working with larger-than-memory datasets on disk
    • 3D imaging datasets
    • imzML ‘processed’ format
  • June 10, 2015Cardinal wins the John M. Chambers Statistical Software Award 2015 presented by the American Statistical Association



  • Visualization of mass spectra and molecular ion images
  • Pre-processing including normalization, baseline correction, peak-picking and alignment
  • Principal components analysis (PCA)
  • Partial least squares discriminant analysis (PLS-DA)
  • Classification based on regularized nearest shrunken centroids
  • Spatial segmentation via regularized nearest shrunken centroid clustering