recipe bioconductor-diffcyt

Differential discovery in high-dimensional cytometry via high-resolution clustering

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/diffcyt.html

License:

MIT + file LICENSE

Recipe:

/bioconductor-diffcyt/meta.yaml

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.

package bioconductor-diffcyt

Versions:

1.18.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.10.0-0,  1.8.6-0,  1.6.0-0,  1.4.3-0,  1.2.23-1, 

1.18.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.10.0-0,  1.8.6-0,  1.6.0-0,  1.4.3-0,  1.2.23-1,  1.2.0-0

Depends:

• bioconductor-complexheatmap >=2.14.0,<2.15.0

• bioconductor-edger >=3.40.0,<3.41.0

• bioconductor-flowcore >=2.10.0,<2.11.0

• bioconductor-flowsom >=2.6.0,<2.7.0

• bioconductor-limma >=3.54.0,<3.55.0

• bioconductor-s4vectors >=0.36.0,<0.37.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• r-base >=4.2,<4.3.0a0

• r-circlize

• r-dplyr

• r-lme4

• r-magrittr

• r-multcomp

• r-reshape2

• r-tidyr

Required By:

• bioconductor-cytofpower

• bioconductor-treekor

• bioconductor-censcyt

Installation

With an activated Bioconda channel (see 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>)

Download stats

Link to this page

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