recipe bioconductor-cn.farms

cn.FARMS - factor analysis for copy number estimation



LGPL (>= 2.0)




biotools: cn.farms, doi: 10.1093/nar/gkr197

This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff.

package bioconductor-cn.farms

(downloads) docker_bioconductor-cn.farms



depends bioconductor-affxparser:


depends bioconductor-affxparser:


depends bioconductor-biobase:


depends bioconductor-biobase:


depends bioconductor-dnacopy:


depends bioconductor-dnacopy:


depends bioconductor-oligo:


depends bioconductor-oligo:


depends bioconductor-oligoclasses:


depends bioconductor-oligoclasses:


depends bioconductor-preprocesscore:


depends bioconductor-preprocesscore:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-dbi:

depends r-ff:

depends r-lattice:

depends r-snow:



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-cn.farms

and update with::

   mamba update bioconductor-cn.farms

To create a new environment, run:

mamba create --name myenvname bioconductor-cn.farms

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-cn.farms/tags`_ for valid values for ``<tag>``)

Download stats