recipe bioconductor-debcam

Deconvolution by Convex Analysis of Mixtures






An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures.

package bioconductor-debcam

(downloads) docker_bioconductor-debcam



depends bioconductor-biobase:


depends bioconductor-biocparallel:


depends bioconductor-summarizedexperiment:


depends openjdk:

depends r-apcluster:

depends r-base:


depends r-corpcor:

depends r-dmwr2:

depends r-geometry:

depends r-nmf:

depends r-nnls:

depends r-pcapp:

depends r-rjava:



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-debcam

and update with::

   mamba update bioconductor-debcam

To create a new environment, run:

mamba create --name myenvname bioconductor-debcam

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

Download stats