tnaake/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 levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.

Getting started

Package details

Bioconductor views ImmunoOncology MassSpectrometry Metabolomics Network Regression
LicenseGPL (>= 3)
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
tnaake/MetNet documentation built on May 9, 2021, 2:29 a.m.