recipe r-dnet

The focus of the dnet by Fang and Gough (2014) <doi:10.1186/s13073-014-0064-8> is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.

Homepage:

http://dnet.r-forge.r-project.org, https://github.com/hfang-bristol/dnet

License:

GPL2 / GPL-2

Recipe:

/r-dnet/meta.yaml

package r-dnet

(downloads) docker_r-dnet

versions:
1.1.7-5,  1.1.7-4,  1.1.7-3,  1.1.7-2,  1.1.7-1,  1.1.7-0,  1.1.6-0,  1.1.5-0,  1.1.4-1, 

1.1.7-5,  1.1.7-4,  1.1.7-3,  1.1.7-2,  1.1.7-1,  1.1.7-0,  1.1.6-0,  1.1.5-0,  1.1.4-1,  1.1.4-0

depends bioconductor-graph:

depends bioconductor-rgraphviz:

depends bioconductor-suprahex:

depends r-base:

>=4.3,<4.4.0a0

depends r-igraph:

depends r-matrix:

requirements:

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 r-dnet

and update with::

   mamba update r-dnet

To create a new environment, run:

mamba create --name myenvname r-dnet

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/r-dnet:<tag>

(see `r-dnet/tags`_ for valid values for ``<tag>``)

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