recipe bioconductor-metnet

Inferring metabolic networks from untargeted high-resolution mass spectrometry data



GPL (>= 3)



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.

package bioconductor-metnet

(downloads) docker_bioconductor-metnet



Required By:


With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-metnet

and update with:

conda update bioconductor-metnet

or use the docker container:

docker pull<tag>

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

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