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Bioinformatics. 2018 Jul 19. doi: 10.1093/bioinformatics/bty650. [Epub ahead of print]

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

Author information

Institute of Computational Biology, Helmholtz Zentrum M├╝nchen, Neuherberg, Germany.
German Center for Diabetes Research (DZD), Neuherberg, Germany.
Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum, Neuherberg, Germany.
Department of Physiology and Biophysics, Weill Cornell Medical College - Qatar, Education City, Doha, Qatar.
Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, USA.



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.

Availability and Implementation: (vignette includes detailed workflow).

Supplementary Information:

Supplementary materials are available at Bioinformatics online.

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