recipe bioconductor-dep

Differential Enrichment analysis of Proteomics data






This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package.

package bioconductor-dep

(downloads) docker_bioconductor-dep



depends bioconductor-complexheatmap:


depends bioconductor-limma:


depends bioconductor-msnbase:


depends bioconductor-summarizedexperiment:


depends bioconductor-vsn:


depends r-assertthat:

depends r-base:


depends r-circlize:

depends r-cluster:

depends r-dplyr:

depends r-dt:

depends r-fdrtool:

depends r-ggplot2:

depends r-ggrepel:

depends r-gridextra:

depends r-imputelcmd:

depends r-purrr:

depends r-rcolorbrewer:

depends r-readr:

depends r-rmarkdown:

depends r-shiny:

depends r-shinydashboard:

depends r-tibble:

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-dep

and update with::

   mamba update bioconductor-dep

To create a new environment, run:

mamba create --name myenvname bioconductor-dep

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

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