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Do, K.T. ; Rasp, D.J.N.P. ; Kastenmüller, G. ; Suhre, K. ; Krumsiek, J.

MoDentify: Phenotype-driven module identification in metabolomics networks at different resolutions.

Bioinformatics 35, 532-534 (2019)
Publ. Version/Full Text Postprint Research data DOI PMC
Open Access Gold (Paid Option)
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Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
ISSN (print) / ISBN 1367-4803
e-ISSN 1367-4811
Journal Bioinformatics
Quellenangaben Volume: 35, Issue: 3, Pages: 532-534 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Bioinformatics and Systems Biology (IBIS)