Using R and Bioconductor for proteomics data analysis
Laurent Gatto, Andy Christoforou
TL;DR
This paper reviews how R and Bioconductor can be effectively used for proteomics data analysis, emphasizing reproducibility and practical use cases.
Contribution
It provides an overview of R tools for proteomics, including use cases, code examples, and guidance on software selection, enhancing reproducible research in the field.
Findings
Demonstrates data input/output and quality control in proteomics
Illustrates quantitative data analysis workflows
Provides resources and code for reproducible proteomics analysis
Abstract
This review presents how R, the popular statistical environment and programming language, can be used in the frame of proteomics data analysis. A short introduction to R is given, with special emphasis on some of the features that make R and its add-on packages a premium software for sound and reproducible data analysis. The reader is also advised on how to find relevant R software for proteomics. Several use cases are then presented, illustrating data input/output, quality control, quantitative proteomics and data analysis. Detailed code and additional links to extensive documentation are available in the freely available companion package RforProteomics.
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