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Bioconductor - MetNet

MetNet

This is the released version of MetNet; for the devel version, see MetNet.

Inferring metabolic networks from untargeted high-resolution mass spectrometry data

Bioconductor version: Release (3.21)

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], Elva Maria Novoa-del-Toro [ctb] ORCID: 0000-0002-6135-5839

Maintainer: Thomas Naake <thomasnaake at googlemail.com>

Citation (from within R, enter citation("MetNet")): Installation

To install this package, start R (version "4.5") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("MetNet")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("MetNet")

Details biocViews ImmunoOncology, MassSpectrometry, Metabolomics, Network, Regression, Software Version 1.26.1 In Bioconductor since BioC 3.8 (R-3.5) (6.5 years) License GPL (>= 3) Depends R (>= 4.1.0), 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) System Requirements URL See More Package Archives

Follow Installation instructions to use this package in your R session.

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