recipe bioconductor-adacgh2

Analysis of big data from aCGH experiments using parallel computing and ff objects



GPL (>= 3)



Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.

package bioconductor-adacgh2

(downloads) docker_bioconductor-adacgh2



depends bioconductor-acgh:


depends bioconductor-acgh:


depends bioconductor-dnacopy:


depends bioconductor-dnacopy:


depends bioconductor-glad:


depends bioconductor-glad:


depends bioconductor-snapcgh:


depends bioconductor-snapcgh:


depends bioconductor-tilingarray:


depends bioconductor-tilingarray:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:


depends r-bit:

depends r-cluster:

depends r-ff:

depends r-waveslim:



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-adacgh2

and update with::

   mamba update bioconductor-adacgh2

To create a new environment, run:

mamba create --name myenvname bioconductor-adacgh2

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-adacgh2/tags`_ for valid values for ``<tag>``)

Download stats