Package Recipe 'bioconductor-genetonic'

recipe bioconductor-genetonic

Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/GeneTonic.html
License:: MIT + file LICENSE
Recipe:: /bioconductor-genetonic/meta.yaml

This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.

package bioconductor-genetonic¶

Versions:

3.4.0-0, 3.0.0-0, 2.6.0-0, 2.4.0-0, 2.2.0-0, 1.6.0-0, 1.4.0-0, 1.2.0-1, 1.2.0-0,

3.4.0-0, 3.0.0-0, 2.6.0-0, 2.4.0-0, 2.2.0-0, 1.6.0-0, 1.4.0-0, 1.2.0-1, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-annotationdbi >=1.72.0,<1.73.0
on bioconductor-complexheatmap >=2.26.0,<2.27.0
on bioconductor-deseq2 >=1.50.0,<1.51.0
on bioconductor-go.db >=3.22.0,<3.23.0
on bioconductor-mosdef >=1.6.0,<1.7.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-backbone
on r-base >=4.5,<4.6.0a0
on r-bs4dash >=2.0.0
on r-circlize
on r-colorspace
on r-colourpicker
on r-complexupset
on r-dendextend
on r-dplyr
on r-dt
on r-dynamictreecut
on r-expm
on r-ggforce
on r-ggplot2 >=3.5.0
on r-ggrepel
on r-ggridges
on r-igraph
on r-matrixstats
on r-plotly
on r-rcolorbrewer
on r-rintrojs
on r-rlang
on r-rmarkdown
on r-scales
on r-shiny
on r-shinyace
on r-shinycssloaders
on r-shinywidgets
on r-tidyr
on r-tippy
on r-viridis
on r-visnetwork

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-genetonic

to add into an existing workspace instead, run:

pixi add bioconductor-genetonic

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-genetonic

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-genetonic

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-genetonic:<tag>

(see bioconductor-genetonic/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-genetonic/README.html)