recipe bioconductor-biocsingular

Singular Value Decomposition for Bioconductor Packages






Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

package bioconductor-biocsingular

(downloads) docker_bioconductor-biocsingular



depends bioconductor-beachmat:


depends bioconductor-beachmat:


depends bioconductor-biocgenerics:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-biocparallel:


depends bioconductor-delayedarray:


depends bioconductor-delayedarray:


depends bioconductor-s4vectors:


depends bioconductor-s4vectors:


depends bioconductor-scaledmatrix:


depends bioconductor-scaledmatrix:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-irlba:

depends r-matrix:

depends r-rcpp:

depends r-rsvd:



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-biocsingular

and update with::

   mamba update bioconductor-biocsingular

To create a new environment, run:

mamba create --name myenvname bioconductor-biocsingular

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

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