Package Recipe 'bioconductor-dep'

recipe bioconductor-dep

Differential Enrichment analysis of Proteomics data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/DEP.html
License:: Artistic-2.0
Recipe:: /bioconductor-dep/meta.yaml

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¶

Versions:

1.32.0-0, 1.28.0-0, 1.24.0-0, 1.22.0-0, 1.20.0-0, 1.16.0-0, 1.14.0-0, 1.12.0-1, 1.12.0-0,

1.32.0-0, 1.28.0-0, 1.24.0-0, 1.22.0-0, 1.20.0-0, 1.16.0-0, 1.14.0-0, 1.12.0-1, 1.12.0-0, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.4.0-0

Depends:

on bioconductor-complexheatmap >=2.26.0,<2.27.0
on bioconductor-limma >=3.66.0,<3.67.0
on bioconductor-msnbase >=2.36.0,<2.37.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-vsn >=3.78.0,<3.79.0
on r-assertthat
on r-base >=4.5,<4.6.0a0
on r-circlize
on r-cluster
on r-dplyr
on r-dt
on r-fdrtool
on r-ggplot2
on r-ggrepel
on r-gridextra
on r-imputelcmd
on r-purrr
on r-rcolorbrewer
on r-readr
on r-rmarkdown
on r-shiny
on r-shinydashboard
on r-tibble
on r-tidyr

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-dep

to add into an existing workspace instead, run:

pixi add bioconductor-dep

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-dep

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-dep

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

docker pull quay.io/biocontainers/bioconductor-dep:<tag>

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-dep/README.html)