bioc-issue-bot
commented
6 years ago Hi @tnaake
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: MetNet
Type: Package
Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data
Version: 0.99.0
Date: 2018-06-06
Authors@R: c(person(given = "Thomas", family = "Naake",
email = "thomasnaake@googlemail.com",
role = c("aut","cre")))
Author: Thomas Naake <thomasnaake@googlemail.com>
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
Depends:
R (>= 3.4.4),
stats (>= 3.4.4)
Imports:
bnlearn (>= 4.3),
methods (>= 3.4.4),
mpmi (>= 0.42),
parallel (>= 3.4.4),
parmigene (>= 1.0.2),
ppcor (>= 1.1),
psych (>= 1.7.8),
rfPermute (>= 2.1.5),
sna (>= 2.4),
stabs (>= 0.6)
Suggests:
BiocGenerics,
igraph (>= 1.1.2),
knitr (>= 1.11)
biocViews: 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-2
RoxygenNote: 6.0.1
mtmorgan
nturaga
tnaake
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