DOI: 10.18129/B9.bioc.MetNet  

Inferring metabolic networks from untargeted high-resolution mass spectrometry data

Bioconductor version: Release (3.17)

MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.

Author: Thomas Naake [aut, cre], Liesa Salzer [ctb]

Maintainer: Thomas Naake <thomasnaake at>

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biocViews ImmunoOncology, MassSpectrometry, Metabolomics, Network, Regression, Software
Version 1.18.0
In Bioconductor since BioC 3.8 (R-3.5) (5 years)
License GPL (>= 3)
Depends R (>= 4.0), S4Vectors(>= 0.28.1), SummarizedExperiment(>= 1.20.0)
Imports bnlearn (>= 4.3), BiocParallel(>= 1.12.0), corpcor (>= 1.6.10), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>= 1.2.15), GENIE3(>= 1.7.0), methods (>= 3.5), parmigene (>= 1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6), stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2)
Suggests BiocGenerics(>= 0.24.0), BiocStyle(>= 2.6.1), glmnet (>= 4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1), Spectra(>= 1.4.1), MsCoreUtils(>= 1.6.0)
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