recipe bioconductor-matter

Out-of-memory dense and sparse signal arrays







biotools: matter, doi: 10.1038/nmeth.3252

Memory-efficient file-based data structures for dense and sparse vectors, matrices, arrays, and lists with applications to nonuniformly sampled signals and spectra.

package bioconductor-matter

(downloads) docker_bioconductor-matter



depends bioconductor-biocgenerics:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-biocparallel:


depends bioconductor-protgenerics:


depends bioconductor-protgenerics:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-biglm:

depends r-digest:

depends r-irlba:

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-matter

and update with::

   mamba update bioconductor-matter

To create a new environment, run:

mamba create --name myenvname bioconductor-matter

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

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