Package Recipe 'bioconductor-metabodynamics'

recipe bioconductor-metabodynamics

Bayesian analysis of longitudinal metabolomics data

Homepage:: https://bioconductor.org/packages/3.22/bioc/html/MetaboDynamics.html
License:: GPL (>= 3)
Recipe:: /bioconductor-metabodynamics/meta.yaml

MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions.

package bioconductor-metabodynamics¶

Versions:

2.0.2-0

Depends:

on bioconductor-ggtree >=4.0.0,<4.1.0
on bioconductor-ggtree >=4.0.4,<4.1.0a0
on bioconductor-keggrest >=1.50.0,<1.51.0
on bioconductor-keggrest >=1.50.0,<1.51.0a0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-s4vectors >=0.48.0,<0.49.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-ape
on r-base >=4.5,<4.6.0a0
on r-bh >=1.66.0
on r-dplyr
on r-dynamictreecut
on r-ggplot2
on r-patchwork
on r-rcpp >=0.12.0
on r-rcppeigen >=0.3.3.3.0
on r-rcppparallel >=5.0.1
on r-rlang
on r-rstan >=2.18.1
on r-rstantools >=2.4.0
on r-stanheaders >=2.18.0
on r-stringr
on r-tidyr
on tbb-devel >=2022.3.0,<2022.4.0a0

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-metabodynamics

to add into an existing workspace instead, run:

pixi add bioconductor-metabodynamics

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-metabodynamics

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-metabodynamics

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-metabodynamics:<tag>

(see bioconductor-metabodynamics/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-metabodynamics/README.html)