Metabomxtr: An R package for mixture-model analysis of non-targeted metabolomics data

Michael Nodzenski, Michael J. Muehlbauer, James R. Bain, Anna C. Reisetter, William L Lowe Jr, Denise M Scholtens*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Non-targeted metabolomics technologies often yield data in which abundance for any given metabolite is observed and quantified for some samples and reported as missing for other samples. Apparent missingness can be due to true absence of the metabolite in the sample or presence at a level below detectability. Mixture-model analysis can formally account for metabolite 'missingness' due to absence or undetectability, but software for this type of analysis in the high-throughput setting is limited. The R package metabomxtr has been developed to facilitate mixture-model analysis of non-targeted metabolomics data in which only a portion of samples have quantifiable abundance for certain metabolites.

Original languageEnglish (US)
Pages (from-to)3287-3288
Number of pages2
Issue number22
StatePublished - Feb 27 2014

ASJC Scopus subject areas

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


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