recipe bioconductor-scdataviz

scDataviz: single cell dataviz and downstream analyses

Homepage

https://bioconductor.org/packages/3.14/bioc/html/scDataviz.html

License

GPL-3

Recipe

/bioconductor-scdataviz/meta.yaml

In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease.

package bioconductor-scdataviz

Versions

1.4.0-0,  1.2.0-0,  1.0.0-1,  1.0.0-0

Depends

• bioconductor-flowcore >=2.6.0,<2.7.0

• bioconductor-s4vectors >=0.32.0,<0.33.0

• bioconductor-singlecellexperiment >=1.16.0,<1.17.0

• r-base >=4.1,<4.2.0a0

• r-corrplot

• r-ggplot2

• r-ggrepel

• r-mass

• r-matrixstats

• r-rcolorbrewer

• r-reshape2

• r-scales

• r-seurat

• r-umap

Required By

Installation

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

conda install bioconductor-scdataviz

and update with:

conda update bioconductor-scdataviz

or use the docker container:

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

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

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-scdataviz/README.html)