recipe bioconductor-dep

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



With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-dep

and update with:

conda update bioconductor-dep

or use the docker container:

docker pull<tag>

(see bioconductor-dep/tags for valid values for <tag>)