recipe bioconductor-xcms

LC-MS and GC-MS Data Analysis



GPL (>= 2) + file LICENSE



Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling.

package bioconductor-xcms

(downloads) docker_bioconductor-xcms



depends bioconductor-biobase:


depends bioconductor-biocgenerics:


depends bioconductor-biocparallel:


depends bioconductor-iranges:


depends bioconductor-massspecwavelet:


depends bioconductor-mscoreutils:


depends bioconductor-msfeatures:


depends bioconductor-msnbase:


depends bioconductor-multtest:


depends bioconductor-mzr:


depends bioconductor-protgenerics:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-lattice:

depends r-plyr:

depends r-rann:

depends r-rcolorbrewer:

depends r-robustbase:



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-xcms

and update with::

   mamba update bioconductor-xcms

To create a new environment, run:

mamba create --name myenvname bioconductor-xcms

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

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