Material for my May Institute 2021 keynote talk
Reproducible research has been on the radar of many of us for several years now. While most agree that reproducibility is a noble feature that we should all strive for, it might at times appear that working reproducibly is an additional difficulty in computational research and data analysis, that already come across as difficult tasks for non-experts.
I will start by motivating my desire to work reproducibly: increasing trust in my work, for myself and my readers. There exist many solutions to support reproducible research, some relatively simple to implement, and others that rely on more complex solutions. While extremely elaborate and powerful, the latter often suffer from a substantial increase in complexity and transparency. At the end of the day, whether the needs for reproducibility are simple or complex, whether they require the utilisation of simple or more elaborate solutions, I would argue that it all boils down to the researcher’s self discipline, and their desire to maximise transparency.
I will demonstrate some of the tools I develop and, more importantly, that I use on a daily basis, to promote reproducibility and transparency in my work. These tools are developed openly and collaboratively in the frame of the R for Mass Spectrometry initiative.
If you would like to be able to reproduce the analysis I will demonstrate, please use a recent version of R (>= 4) and install the following packages.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("msdata")
BiocManager::install("mzR")
BiocManager::install("lgatto/ProtGenerics")
BiocManager::install("lgatto/rpx")
BiocManager::install("RforMassSpectrometry/MsExperiment")
BiocManager::install("RforMassSpectrometry/MsCoreUtils")
BiocManager::install("RforMassSpectrometry/PSM")
BiocManager::install("RforMassSpectrometry/Spectra")
BiocManager::install("RforMassSpectrometry/SpectraVis")
If you hit any installation issues, don’t hesitate to open a GitHub issue or contact me by email.
To familiarise yourself with the R for Mass Spectrometry infrastructure:
To learn about the raw data infrastructure:
(Even though this paper describes the MSnbase raw data backends, it also applies to the Spectra package.)
The material in this repositry is made available under a Creative Commons Attribution 4.0 International License.