recipe bioconductor-glmgampoi

Fit a Gamma-Poisson Generalized Linear Model






Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

package bioconductor-glmgampoi

(downloads) docker_bioconductor-glmgampoi



depends bioconductor-beachmat:


depends bioconductor-biocgenerics:


depends bioconductor-delayedarray:


depends bioconductor-delayedmatrixstats:


depends bioconductor-hdf5array:


depends bioconductor-matrixgenerics:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-matrixstats:

depends r-rcpp:

depends r-rcpparmadillo:

depends r-rlang:

depends r-vctrs:



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-glmgampoi

and update with::

   mamba update bioconductor-glmgampoi

To create a new environment, run:

mamba create --name myenvname bioconductor-glmgampoi

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

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