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 a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/diffcyt.html

License

MIT + file LICENSE

Recipe

/bioconductor-diffcyt/meta.yaml

package bioconductor-diffcyt

(downloads) docker_bioconductor-diffcyt

Versions

1.2.23-1, 1.2.0-0

Depends bioconductor-complexheatmap

>=1.20.0,<1.21.0

Depends bioconductor-edger

>=3.24.0,<3.25.0

Depends bioconductor-flowcore

>=1.48.0,<1.49.0

Depends bioconductor-flowsom

>=1.14.0,<1.15.0

Depends bioconductor-limma

>=3.38.0,<3.39.0

Depends bioconductor-s4vectors

>=0.20.0,<0.21.0

Depends bioconductor-summarizedexperiment

>=1.12.0,<1.13.0

Depends r-base

>=3.5.1,<3.5.2.0a0

Depends r-circlize

Depends r-dplyr

Depends r-lme4

Depends r-magrittr

Depends r-multcomp

Depends r-reshape2

Depends r-tidyr

Requirements

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>)