recipe r-ramclustr

A feature clustering algorithm for non-targeted mass spectrometric metabolomics data. This method is compatible with gas and liquid chromatography coupled mass spectrometry, including indiscriminant tandem mass spectrometry data <DOI: 10.1021/ac501530d>.



GPL2 / GPL-2.0-or-later



package r-ramclustr

(downloads) docker_r-ramclustr



depends bioconductor-msnbase:

depends bioconductor-pcamethods:

depends r-base:


depends r-biocmanager:

depends r-dynamictreecut:

depends r-e1071:

depends r-fastcluster:

depends r-ggplot2:

depends r-gplots:

depends r-httr:

depends r-interpretmsspectrum:

depends r-jsonlite:

depends r-rcurl:

depends r-readxl:

depends r-stringi:

depends r-stringr:

depends r-webchem:

depends r-xml2:



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-ramclustr

and update with::

   mamba update r-ramclustr

To create a new environment, run:

mamba create --name myenvname r-ramclustr

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

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