recipe bioconductor-glmsparsenet

Network Centrality Metrics for Elastic-Net Regularized Models

Homepage:

https://bioconductor.org/packages/3.14/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:

1.12.0-0,  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 >=2.50.0,<2.51.0

• bioconductor-multiassayexperiment >=1.20.0,<1.21.0

• bioconductor-summarizedexperiment >=1.24.0,<1.25.0

• r-base >=4.1,<4.2.0a0

• r-dplyr

• r-forcats

• r-futile.logger

• r-ggplot2

• r-glmnet

• r-glue

• r-httr

• r-loose.rock >=1.0.12

• r-matrix

• r-readr

• r-reshape2

• r-sparsebn

• r-sparsebnutils

• r-stringr

• r-survminer

Required By:

Installation

With an activated Bioconda channel (see 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>)

Download stats

Link to this page

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