MALDIquantReport: An R package for user-friendly data treatment and automated report generation for Ambient Ionisation Mass Spectrometry (AIMS) data Guillen-Alonso Hector 1,2 and Winkler, Robert 1 1 Department of Biotechnology and Biochemistry and Department Unit of Advanced Genomic, Center for Research and Advanced Studies (CINVESTAV) Irapuato, Km, Mexico, 2 Department of Biochemical Engineering, Nacional Technological Institute, Mexico Ambient Ionization Mass Spectrometry (AIMS) data require extensive pre-treatment previous to statistical analysis due to ambient interference. Data processing is a challenging task for inexperienced analysts. The main questions for adequate data pre-treatment and reporting are: 1) How to define workflow parameters?, and 2) What to report? Here, we demonstrate a tool to guide new users through the AIMS data pre-treatment workflow. MALDIquantReport accompanies novice users through the workflow with a minimum of technical knowledge necessary. Every step shows the graphical output for visualising what is happening. The R package is based on MALDIquant [MALDIquant: a versatile R package for the analysis of mass spectrometry data from Sebastian Gibb, 2017]. Expert users can run MALDIquantReport automatically, loading their optimized settings. Settings can be easily edited in spreadsheet software. The comma-separated value (CSV) settings file in the data directory is automatically loaded and used by the workflow. After data pre-treatment, MALDIquantReport exports an aligned feature matrix in CSV format. The matrix is also available as an R object for further statistical analyses. New users are guided by MALDIquantReport in the complete workflow and can observe visually how selected parameters will affect the data. For motivating the user to try new parameters, every step is looping until the user finds suitable settings. The final parameters are applied in the workflow and documented in the final analysis report. MALDIquantReport generates the report in PDF, with all data processing steps and parameters applied, and plots for every workflow step. This report can be added directly to scientific reports for documenting the data analysis. In addition, the CSV settings file can be attached as supplementary material. References 1. Gibb, S., & Strimmer, K. (2012). MALDIquant : a versatile R package for the analysis of mass spectrometry data. 28(17), 2270–2271. https://doi.org/10.1093/bioinformatics/bts447 2. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
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© The Author(s), 2022
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