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.
NEWS
- October, 2019 — We are teaching Cardinal Workshops at the following mass spectrometry imaging conferences:
- October 11, 2019 – Asilomar Conference, hosted by ASMS and IMSS
- October 28, 2019 – OurCon VII, hosted by MSIS
- May 3, 2019 — Cardinal 2.2 is officially live with Bioconductor 3.9! This is a major update with many changes. Cardinal 2.0 set the groundwork for a migration to the new MSImagingExperiment class, and Cardinal 2.2 fulfills that promise with many new updates, changes, and new features:
- Defaults for data import have changed:
- Datasets are imported as MSImagingExperiment objects
- Spectra from imported datasets are not loaded into memory; they are loaded on-access
- All statistical methods (including PCA, PLS, spatial k-means, and spatial shrunken centroids) are now fully supported for MSImagingExperiment
- New statistical methods including spatial-DGMM and hypothesis testing via means-summarized linear models and segmentation-based linear models
- Pervasive support for parallel computation via BiocParallel
- New vignettes documenting both basic use and statistical methods for MSImagingExperiment
- Visualization enhancements:
- Colorkeys for images are now plotted on the side
- Default colorscale for images is now “viridis”
- “Dark mode”
- Improved simulation of spectra and imaging experiments
- Out-of-memory enhancements in matter backend
- Exhaustive list of changes documented here on Bioconductor NEWS
- Defaults for data import have changed:
- 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, 2018 — Cardinal 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, 2016 — Cardinal 1.4 is available on Bioconductor 3.3
- December 16, 2015 — Cardinal 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, 2015 — Cardinal wins the John M. Chambers Statistical Software Award 2015 presented by the American Statistical Association
DATA FORMATS
- Analyze 7.5 (ABSciex and others)
- imzML (converters available at http://www.imzml.org)
FEATURES
- 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
CITING CARDINAL
CITING SPATIAL SHRUNKEN CENTROIDS
- Bemis, K. D., Harry, A., Eberlin, L. S., Ferreira, C. R., van de Ven, S. M., Mallick, P., et al. (2016). Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments. Molecular & Cellular Proteomics, 15(5), 1761–1772. http://doi.org/10.1074/mcp.O115.053918