recipe bioconductor-metnet

Inferring metabolic networks from untargeted high-resolution mass spectrometry data

Homepage

https://bioconductor.org/packages/3.11/bioc/html/MetNet.html

License

GPL (>= 3)

Recipe

/bioconductor-metnet/meta.yaml

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.

package bioconductor-metnet

Versions

1.6.0-0,  1.4.0-0,  1.2.0-1,  1.0.0-0

Depends

• bioconductor-biocparallel >=1.22.0,<1.23.0

• bioconductor-genie3 >=1.10.0,<1.11.0

• r-base >=4.0,<4.1.0a0

• r-bnlearn >=4.3

• r-mpmi >=0.42

• r-parmigene >=1.0.2

• r-ppcor >=1.1

• r-sna >=2.4

• r-stabs >=0.6

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-metnet

and update with:

conda update bioconductor-metnet

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-metnet:<tag>

(see bioconductor-metnet/tags for valid values for <tag>)

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-metnet/README.html)