Chapter 1 Preamble

The aim of the R for Mass Spectrometry initiative is to provide efficient, thoroughly documented, tested and flexible R software for the analysis and interpretation of high throughput mass spectrometry assays, including proteomics and metabolomics experiments. The project formalises the longtime collaborative development efforts of its core members under the RforMassSpectrometry organisation to facilitate dissemination and accessibility of their work.

The *R for Mass Spectrometry* intiative sticker, designed by Johannes Rainer.Figure 1.1: The R for Mass Spectrometry intiative sticker, designed by Johannes Rainer.

This material introduces participants to the analysis and exploration of mass spectrometry (MS) based proteomics data using R and Bioconductor. The course will cover all levels of MS data, from raw data to identification and quantitation data, up to the statistical interpretation of a typical shotgun MS experiment and will focus on hands-on tutorials. At the end of this course, the participants will be able to manipulate MS data in R and use existing packages for their exploratory and statistical proteomics data analysis.

Targeted audience and assumed background

The course material is targeted to either proteomics practitioners or data analysts/bioinformaticians that would like to learn how to use R and Bioconductor to analyse proteomics data. Familiarity with MS or proteomics in general is desirable, but not essential as we will walk through and describe a typical MS data as part of learning about the tools. A working knowledge of R (R syntax, commonly used functions, basic data structures such as data frames, vectors, matrices, … and their manipulation) is required. Familiarity with other Bioconductor omics data classes and the tidyverse syntax is useful, but not necessary.


This material uses the latest version of the R for Mass Spectrometry package and their dependencies. It might this be possible that even the latest Bioconductor stable version aren’t recent enough.

To install the latest versions of all the package, please use R version 4.2 and execute:

if (!requireNamespace("BiocManager", quietly = TRUE))


To compile and render the teaching material, you will also need the (slighly modified) Modern Statistics for Model Biology (msmb) HTML Book Style by Mike Smith:



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