MetNet: Inferring metabolic networks from untargeted high-resolution mass spectrometry data

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 matrices are combined to form a adjacency matrix inferred from both quantitative and structure information.

Package details

AuthorThomas Naake [aut, cre]
Bioconductor views ImmunoOncology MassSpectrometry Metabolomics Network Regression
MaintainerThomas Naake <>
LicenseGPL (>= 3)
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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MetNet documentation built on Nov. 8, 2020, 7:34 p.m.