Package Recipe 'bioconductor-metnet'

recipe bioconductor-metnet

Inferring metabolic networks from untargeted high-resolution mass spectrometry data

Homepage:: https://bioconductor.org/packages/3.20/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 levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.

package bioconductor-metnet¶

Versions:

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-0, 1.12.0-0, 1.8.0-1, 1.8.0-0, 1.6.0-0,

1.28.0-0, 1.24.0-0, 1.20.0-0, 1.18.0-0, 1.16.0-0, 1.12.0-0, 1.8.0-1, 1.8.0-0, 1.6.0-0, 1.4.0-0, 1.2.0-1, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-genie3 >=1.32.0,<1.33.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-base >=4.5,<4.6.0a0
on r-bnlearn >=4.3
on r-corpcor >=1.6.10
on r-dplyr >=1.0.3
on r-genenet >=1.2.15
on r-ggplot2 >=3.3.3
on r-parmigene >=1.0.2
on r-psych >=2.1.6
on r-rlang >=0.4.10
on r-stabs >=0.6
on r-tibble >=3.0.5
on r-tidyr >=1.1.2

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-metnet

to add into an existing workspace instead, run:

pixi add bioconductor-metnet

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-metnet

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-metnet

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

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)