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Metabomxtr: an R package for mixture-model analysis of non-targeted metabolomics data
Michael Nodzenski1, Michael J Muehlbauer1, James R Bain2
1Department of Preventive Medicine, Division of Biostatistics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, Sarah W. Stedman Nutrition and Metabolism Center, Duke Molecular Physiology Institute and Division of Endocrinology, Metabolism, and Nutrition, Department of Medicine, Duke University Medical Center, Durham, NC 27704 and Department of Medicine, Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611.
Summary:
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.
Availability And Implementation:
metabomxtr is available through Bioconductor. It is released under the GPL-2 license.
Contact:
dscholtens@northwestern.edu
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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