recipe r-smartsva

Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.

Homepage:

https://CRAN.R-project.org/package=SmartSVA

License:

GPL3 / GPL-3

Recipe:

/r-smartsva/meta.yaml

package r-smartsva

versions:

0.1.3-8,  0.1.3-7,  0.1.3-6,  0.1.3-5,  0.1.3-4,  0.1.3-3,  0.1.3-2,  0.1.3-1,  0.1.3-0

depends bioconductor-sva:

depends libgcc-ng:

>=12

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-isva:

depends r-rcpp:

depends r-rcppeigen:

depends r-rspectra:

requirements:

Installation

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 r-smartsva

and update with::

   mamba update r-smartsva

To create a new environment, run:

mamba create --name myenvname r-smartsva

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 quay.io/biocontainers/r-smartsva:<tag>

(see `r-smartsva/tags`_ for valid values for ``<tag>``)

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