recipe bioconductor-asics

Automatic Statistical Identification in Complex Spectra



GPL (>= 2)



With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) <doi:10.1007/s11306-017-1244-5>.

package bioconductor-asics

(downloads) docker_bioconductor-asics



depends bioconductor-biocparallel:


depends bioconductor-pepsnmr:


depends bioconductor-ropls:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-ggplot2:

depends r-glmnet:

depends r-gridextra:

depends r-matrix:

depends r-mvtnorm:

depends r-plyr:

depends r-quadprog:

depends r-zoo:



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-asics

and update with::

   mamba update bioconductor-asics

To create a new environment, run:

mamba create --name myenvname bioconductor-asics

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

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