recipe bioconductor-cocoa

Coordinate Covariation Analysis

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/COCOA.html

License:

GPL-3

Recipe:

/bioconductor-cocoa/meta.yaml

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

package bioconductor-cocoa

versions:

2.16.0-0,  2.14.0-0,  2.12.0-0,  2.8.0-0,  2.6.0-0,  2.4.0-1,  2.4.0-0,  2.2.0-0,  2.0.0-0, 

2.16.0-0,  2.14.0-0,  2.12.0-0,  2.8.0-0,  2.6.0-0,  2.4.0-1,  2.4.0-0,  2.2.0-0,  2.0.0-0,  1.2.0-1,  1.0.1-0

depends bioconductor-biobase:

>=2.62.0,<2.63.0

depends bioconductor-biocgenerics:

>=0.48.0,<0.49.0

depends bioconductor-complexheatmap:

>=2.18.0,<2.19.0

depends bioconductor-genomicranges:

>=1.54.0,<1.55.0

depends bioconductor-iranges:

>=2.36.0,<2.37.0

depends bioconductor-mira:

>=1.24.0,<1.25.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends r-base:

>=4.3,<4.4.0a0

depends r-data.table:

depends r-fitdistrplus:

depends r-ggplot2:

depends r-simplecache:

depends r-tidyr:

requirements:

additional platforms:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-cocoa

and update with::

   mamba update bioconductor-cocoa

To create a new environment, run:

mamba create --name myenvname bioconductor-cocoa

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

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

(see `bioconductor-cocoa/tags`_ for valid values for ``<tag>``)

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