DESCRIPTION

Package: MetNet
Type: Package
Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data
Version: 1.5.3
Date: 2020-03-07
Authors@R: c(person(given = "Thomas", family = "Naake",
    email = "thomasnaake@googlemail.com",
    role = c("aut","cre")))
VignetteBuilder: knitr
Depends:
    R (>= 3.6)
Imports:
    bnlearn (>= 4.3), 
    BiocParallel (>= 1.12.0),
    GENIE3 (>= 1.7.0),
    methods (>= 3.5),
    mpmi (>= 0.42), 
    parmigene (>= 1.0.2),
    ppcor (>= 1.1),
    sna (>= 2.4),
    stabs (>= 0.6),
    stats (>= 3.6)
Suggests:
    BiocGenerics (>= 0.24.0),
    BiocStyle (>= 2.6.1), 
    glmnet (>= 2.0-18),
    igraph (>= 1.1.2), 
    knitr (>= 1.11),
    rmarkdown (>= 1.15),
    testthat (>= 2.2.1)
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression
Description: 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. 
License: GPL (>= 3)
RoxygenNote: 6.1.1