title: Using R and Bioconductor for proteomics data 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 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. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
authors.affiliation: Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK. Electronic address: lg390@cam.ac.uk.
authors.forename: Laurent
authors.initials: L
authors.lastname: Gatto
authors.forename: Andy
authors.initials: A
authors.lastname: Christoforou
mesh_terms: D000595:Amino Acid Sequence; D013058:Mass Spectrometry; D008969:Molecular Sequence Data; D010751:Phosphopyruvate Hydratase / Q000737:chemistry; D011381:Programming Languages; D040901:Proteomics; D011786:Quality Control
chemical_list: D010751:Phosphopyruvate Hydratase
keywords: Data analysis statistics; Mass spectrometry; Quality control; Quantitative proteomics; Software