MSPrep-Summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data

Grant Hughes, Charmion Cruickshank-Quinn, Richard Reisdorph, Sharon Lutz, Irina Petrache, Nichole Reisdorph, Russell Bowler, Katerina Kechris

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

Motivation: Although R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed the MSPrep R package to complement other packages by providing these additional steps, implementing a selection of popular normalization algorithms and generating diagnostics to help guide investigators in their analyses. Availability: http://www.sourceforge.net/projects/msprepContact: Supplementary Information: Supplementary materials are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)133-134
Number of pages2
JournalBioinformatics
Volume30
Issue number1
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Hughes, G., Cruickshank-Quinn, C., Reisdorph, R., Lutz, S., Petrache, I., Reisdorph, N., Bowler, R., & Kechris, K. (2014). MSPrep-Summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data. Bioinformatics, 30(1), 133-134. https://doi.org/10.1093/bioinformatics/btt589