recipe bioconductor-msstatstmt

Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling






The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine.

package bioconductor-msstatstmt

(downloads) docker_bioconductor-msstatstmt



depends bioconductor-limma:


depends bioconductor-msstats:


depends bioconductor-msstatsconvert:


depends r-base:


depends r-checkmate:

depends r-data.table:

depends r-ggplot2:

depends r-lme4:

depends r-lmertest:



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-msstatstmt

and update with::

   mamba update bioconductor-msstatstmt

To create a new environment, run:

mamba create --name myenvname bioconductor-msstatstmt

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

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