recipe bioconductor-scdd

Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

Homepage

https://bioconductor.org/packages/3.11/bioc/html/scDD.html

License

GPL-2

Recipe

/bioconductor-scdd/meta.yaml

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

package bioconductor-scdd

Versions

1.12.0-0,  1.10.0-0,  1.8.0-1,  1.6.0-0

Depends

• bioconductor-biocparallel >=1.22.0,<1.23.0

• bioconductor-ebseq >=1.28.0,<1.29.0

• bioconductor-s4vectors >=0.26.0,<0.27.0

• bioconductor-scran >=1.16.0,<1.17.0

• bioconductor-singlecellexperiment >=1.10.0,<1.11.0

• bioconductor-summarizedexperiment >=1.18.0,<1.19.0

• r-arm

• r-base >=4.0,<4.1.0a0

• r-fields

• r-ggplot2

• r-mclust

• r-outliers

Required By

Installation

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

conda install bioconductor-scdd

and update with:

conda update bioconductor-scdd

or use the docker container:

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

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

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-scdd/README.html)