Package: MetNet
Type: Package
Title: Inferring metabolic networks from untargeted high-resolution
        mass spectrometry data
Version: 1.5.3
Date: 2020-01-12
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
git_url: https://git.bioconductor.org/packages/MetNet
git_branch: master
git_last_commit: a53de1a
git_last_commit_date: 2020-01-12
Date/Publication: 2020-01-27
NeedsCompilation: no
Packaged: 2020-01-28 07:17:40 UTC; biocbuild
Author: Thomas Naake [aut, cre]
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
Built: R 4.0.0; ; 2020-01-28 17:09:13 UTC; windows
