recipe bioconductor-dcgsa

Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles







biotools: dcgsa, doi: 10.1038/nmeth.3252

Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.

package bioconductor-dcgsa

(downloads) docker_bioconductor-dcgsa



depends bioconductor-biocparallel:


depends r-base:


depends r-matrix:



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-dcgsa

and update with::

   mamba update bioconductor-dcgsa

To create a new environment, run:

mamba create --name myenvname bioconductor-dcgsa

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

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