Abstract
Mass spectrometry (MS) is a key technology used across multiple fields, including biomedical research and life sciences. Technological advancements result in increasingly large and complex data sets and analyses must be tailored to the experimental and instrumental setups.
Excellent software libraries for such data analysis are available in both R and Python, including R packages from the RforMassSpectrometry initiative and Python libraries like matchms, spectrum_utils, Pyteomics and pyOpenMS. Having partially complimentary functionality, these software cover different aspects of MS-based proteomics or metabolomics data analysis. Integration of basic data types and functionality of the two programming languages are provided by the reticulate R package.
The SpectriPy Bioconductor package builds upon reticulate and provides additional functionality to efficiently translate between R and Python MS data structures. It can convert between R’s Spectra::Spectra and Python’s matchms.Spectrum and spectrum_utils.spectrum.MsmsSpectrum objects hence enabling and simplifying the integration of R and Python for MS data analysis.
In this workshop we present how reproducible cross-language MS data analyses can be conducted with the SpectriPy package. Such analyses will empower data analysts who can thus benefit from the full power of algorithms in both programming languages.