recipe bioconductor-qmtools

Quantitative Metabolomics Data Processing Tools






The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data.

package bioconductor-qmtools

(downloads) docker_bioconductor-qmtools



depends bioconductor-limma:


depends bioconductor-mscoreutils:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-ggplot2:

depends r-heatmaply:

depends r-igraph:

depends r-patchwork:

depends r-rlang:

depends r-scales:

depends r-vim:



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-qmtools

and update with::

   mamba update bioconductor-qmtools

To create a new environment, run:

mamba create --name myenvname bioconductor-qmtools

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

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