recipe bioconductor-gaga

GaGa hierarchical model for high-throughput data analysis



GPL (>= 2)




biotools: gaga, doi: 10.1214/09-aoas244

Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

package bioconductor-gaga

(downloads) docker_bioconductor-gaga



depends bioconductor-biobase:


depends bioconductor-biobase:


depends bioconductor-ebarrays:


depends bioconductor-ebarrays:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:


depends r-coda:

depends r-mgcv:



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-gaga

and update with::

   mamba update bioconductor-gaga

To create a new environment, run:

mamba create --name myenvname bioconductor-gaga

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

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