recipe bioconductor-deqms

a tool to perform statistical analysis of differential protein expression for quantitative proteomics data.






DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.

package bioconductor-deqms

(downloads) docker_bioconductor-deqms



depends bioconductor-limma:


depends r-base:


depends r-ggplot2:

depends r-matrixstats:



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-deqms

and update with::

   mamba update bioconductor-deqms

To create a new environment, run:

mamba create --name myenvname bioconductor-deqms

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

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