Package Recipe 'bioconductor-cocoa'

recipe bioconductor-cocoa

Coordinate Covariation Analysis

Homepage:: https://bioconductor.org/packages/3.20/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:

2.24.0-0, 2.20.0-0, 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,

2.24.0-0, 2.20.0-0, 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:

on bioconductor-biobase >=2.70.0,<2.71.0
on bioconductor-biocgenerics >=0.56.0,<0.57.0
on bioconductor-complexheatmap >=2.26.0,<2.27.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-mira >=1.32.0,<1.33.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on r-base >=4.5,<4.6.0a0
on r-data.table
on r-fitdistrplus
on r-ggplot2
on r-simplecache
on r-tidyr

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-cocoa

to add into an existing workspace instead, run:

pixi add bioconductor-cocoa

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-cocoa

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-cocoa

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

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-cocoa/README.html)