recipe bioconductor-macarron

Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets






Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition.

package bioconductor-macarron

(downloads) docker_bioconductor-macarron



depends bioconductor-biocparallel:


depends bioconductor-delayedarray:


depends bioconductor-maaslin2:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-data.table:

depends r-dynamictreecut:

depends r-ff:

depends r-logging:

depends r-plyr:

depends r-psych:

depends r-rcurl:

depends r-rjsonio:

depends r-wgcna:

depends r-xml2:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-macarron

and update with::

   mamba update bioconductor-macarron

To create a new environment, run:

mamba create --name myenvname bioconductor-macarron

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-macarron/tags`_ for valid values for ``<tag>``)

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