MSstats and Cardinal: Next Generation Statistical Mass Spectrometry in R

MSstats is a family of open-source R/Bioconductor packages for statistical relative quantification of peptides and proteins in mass spectrometry-based proteomics. MSstats is applicable to experiments with arbitrary complex designs (factorial experiments, paired designs, time course), data acquired with shotgun DDA, data independent DIA/SWATH, PRM or targeted SRM workflows, and label-free or label-based (e.g., TMT labeling) workflows.

The core of MSstats is its state-of-the-art statistical models and algorithms that address technological aspects specific to mass spectrometry-based proteomics. The functionalities include data visualization, normalization, transformation, detecting differentially abundant proteins, estimating protein abundance, or detecting sites with differential post-translational modifications. It also can detect system suitability and quality control for mass spectrometric assays, characterize mass spectrometric assays (e.g., limits of detection, dynamic range), and plan future experiments (sample size calculation for detection of differentially abundant proteins or predictive proteins).

MSstats takes as input a list of identified and quantified spectral features. It interfaces with most currently used open-source and commercial tools (e.g., Skyline, MaxQuant, OpenMS, Spectronaut, ProteomeDiscoverer).

Cardinal is a family of open source R/Bioconductor packages for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples, such as tissues. Technology-specific issues, such as data accessibility, difficulties of mapping spectra between samples, and complexities of analyte ionization and fragmentation, require specialized analysis tools for MSI.

Cardinal supports 2- and 3-dimensional MSI experiments with multiple tissues and conditions, and complex designs, as well as matrix-assisted laser desorption/ionization and desorption electrospray ionization-based workflows.

Cardinal’s functionalities include image visualization, image segmentation, image classification, and detection of differentially abundant ions across conditions.

Due to large sizes of raw MSI data, the backend to Cardinal supports direct interactions with larger-than-memory datasets, stored in an arbitrary number of files in arbitrary formats. Matter supports reproducible research by minimizing the need of converting and storing data in multiple formats.