Using R and Bioconductor for proteomics data analysis

Biochim Biophys Acta. 2014 Jan;1844(1 Pt A):42-51. doi: 10.1016/j.bbapap.2013.04.032. Epub 2013 May 18.

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.

Keywords: Data analysis statistics; Mass spectrometry; Quality control; Quantitative proteomics; Software.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Amino Acid Sequence
  • Mass Spectrometry
  • Molecular Sequence Data
  • Phosphopyruvate Hydratase / chemistry
  • Programming Languages*
  • Proteomics*
  • Quality Control

Substances

  • Phosphopyruvate Hydratase