recipe bioconductor-glmsparsenet

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”.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/glmSparseNet.html

License

GPL (>=3)

Recipe

/bioconductor-glmsparsenet/meta.yaml

package bioconductor-glmsparsenet

Versions

1.2.0-0, 1.0.0-0

Depends

• bioconductor-biomart >=2.40.0,<2.41.0

• bioconductor-multiassayexperiment >=1.10.0,<1.11.0

• bioconductor-stringdb >=1.24.0,<1.25.0

• bioconductor-summarizedexperiment >=1.14.0,<1.15.0

• r-base >=3.6,<3.7.0a0

• r-dplyr

• r-forcats

• r-futile.logger

• r-ggplot2

• r-glmnet

• r-loose.rock >=1.0.12

• r-matrix

• r-readr

• r-reshape2

• r-rlang

• r-sparsebn

• r-sparsebnutils

• r-stringr

• r-survminer

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-glmsparsenet

and update with:

conda update bioconductor-glmsparsenet

or use the docker container:

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

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

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-glmsparsenet/README.html)