Abstract
Summary: 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: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.
| Original language | English |
|---|---|
| Pages (from-to) | 532-534 |
| Number of pages | 3 |
| Journal | Bioinformatics (Oxford, England) |
| Volume | 35 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Feb 2019 |
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ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics
Cite this
MoDentify : phenotype-driven module identification in metabolomics networks at different resolutions. / Do, Kieu Trinh; Rasp, David J.N.P.; Kastenmüller, Gabi; Suhre, Karsten; Krumsiek, Jan.
In: Bioinformatics (Oxford, England), Vol. 35, No. 3, 01.02.2019, p. 532-534.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - MoDentify
T2 - phenotype-driven module identification in metabolomics networks at different resolutions
AU - Do, Kieu Trinh
AU - Rasp, David J.N.P.
AU - Kastenmüller, Gabi
AU - Suhre, Karsten
AU - Krumsiek, Jan
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Summary: 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: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.
AB - Summary: 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: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.
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U2 - 10.1093/bioinformatics/bty650
DO - 10.1093/bioinformatics/bty650
M3 - Article
C2 - 30032270
AN - SCOPUS:85061162778
VL - 35
SP - 532
EP - 534
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 3
ER -