recipe bioconductor-metabcombiner

Method for Combining LC-MS Metabolomics Feature Measurements






This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods.

package bioconductor-metabcombiner

(downloads) docker_bioconductor-metabcombiner



depends bioconductor-s4vectors:


depends bioconductor-s4vectors:


depends libblas:


depends libgcc-ng:


depends liblapack:


depends r-base:


depends r-caret:

depends r-dplyr:


depends r-matrixstats:

depends r-mgcv:

depends r-rlang:

depends r-tidyr:



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-metabcombiner

and update with::

   mamba update bioconductor-metabcombiner

To create a new environment, run:

mamba create --name myenvname bioconductor-metabcombiner

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

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