recipe bioconductor-diffcyt

Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on (i) high-resolution clustering and (ii) empirical Bayes moderated tests adapted from transcriptomics.

Homepage

https://bioconductor.org/packages/3.8/bioc/html/diffcyt.html

License

MIT + file LICENSE

Recipe

/bioconductor-diffcyt/meta.yaml

package bioconductor-diffcyt

Versions

1.2.0-0

Depends

• bioconductor-complexheatmap >=1.20.0,<1.21.0

• bioconductor-edger >=3.24.0,<3.25.0

• bioconductor-flowcore >=1.48.0,<1.49.0

• bioconductor-flowsom >=1.14.0,<1.15.0

• bioconductor-limma >=3.38.0,<3.39.0

• bioconductor-s4vectors >=0.20.0,<0.21.0

• bioconductor-summarizedexperiment >=1.12.0,<1.13.0

• r-base >=3.5.1,<3.5.2.0a0

• r-circlize

• r-dplyr

• r-lme4

• r-magrittr

• r-multcomp

• r-reshape2

• r-tidyr

Required By

Installation

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

conda install bioconductor-diffcyt

and update with:

conda update bioconductor-diffcyt

or use the docker container:

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

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