recipe bioconductor-omicrexposome

Exposome and omic data associatin and integration analysis






omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).

package bioconductor-omicrexposome

(downloads) docker_bioconductor-omicrexposome



depends bioconductor-biobase:


depends bioconductor-limma:


depends bioconductor-multidataset:


depends bioconductor-omicade4:


depends bioconductor-rexposome:


depends bioconductor-summarizedexperiment:


depends bioconductor-sva:


depends r-base:


depends r-ggplot2:

depends r-ggrepel:

depends r-gridextra:

depends r-isva:

depends r-pma:

depends r-smartsva:

depends r-stringr:



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-omicrexposome

and update with::

   mamba update bioconductor-omicrexposome

To create a new environment, run:

mamba create --name myenvname bioconductor-omicrexposome

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-omicrexposome/tags`_ for valid values for ``<tag>``)

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