Mass spectrometry (MS) is a powerful tool for measuring biomolecules, but the data produced is often difficult to handle computationally because it is stored as a ragged array. In R, this format is typically encoded in complex S4 objects built around environments, requiring an extensive background in R to perform even simple tasks. However, the adoption of tidy data (Wickham 2014) provides an alternate data structure that is highly intuitive and works neatly with base R functions and common packages, as well as other programming languages. Here, we discuss the current state of R-based MS data processing, the convenience and challenges of integrating tidy data techniques into MS data processing, and present RaMS, a package that produces tidy representations of MS data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-050.zip
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For attribution, please cite this work as
Kumler & Ingalls, "The R Journal: Tidy Data Neatly Resolves Mass-Spectrometry's Ragged Arrays", The R Journal, 2022
BibTeX citation
@article{RJ-2022-050,
author = {Kumler, William and Ingalls, Anitra E.},
title = {The R Journal: Tidy Data Neatly Resolves Mass-Spectrometry's Ragged Arrays},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-050},
doi = {10.32614/RJ-2022-050},
volume = {14},
issue = {3},
issn = {2073-4859},
pages = {193-202}
}