recipe bioconductor-glmsparsenet

Network Centrality Metrics for Elastic-Net Regularized Models

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-glmsparsenet/meta.yaml

glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".

package bioconductor-glmsparsenet

versions:

1.20.0-0,  1.18.0-0,  1.16.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.8.0-0,  1.6.0-0,  1.4.0-0, 

1.20.0-0,  1.18.0-0,  1.16.0-0,  1.12.0-0,  1.10.0-0,  1.8.0-1,  1.8.0-0,  1.6.0-0,  1.4.0-0,  1.2.0-0,  1.0.0-0

depends bioconductor-biomart:

>=2.58.0,<2.59.0

depends bioconductor-multiassayexperiment:

>=1.28.0,<1.29.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-base:

>=4.3,<4.4.0a0

depends r-digest:

depends r-dplyr:

depends r-forcats:

depends r-futile.logger:

depends r-futile.options:

depends r-ggplot2:

depends r-glmnet:

depends r-glue:

depends r-httr:

depends r-matrix:

depends r-readr:

depends r-reshape2:

depends r-stringr:

depends r-survminer:

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 bioconductor-glmsparsenet

and update with::

   mamba update bioconductor-glmsparsenet

To create a new environment, run:

mamba create --name myenvname bioconductor-glmsparsenet

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-glmsparsenet:<tag>

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

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