glmSparseNet

DOI: 10.18129/B9.bioc.glmSparseNet    

This package is for version 3.11 of Bioconductor; for the stable, up-to-date release version, see glmSparseNet.

Network Centrality Metrics for Elastic-Net Regularized Models

Bioconductor version: 3.11

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".

Author: André Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb]

Maintainer: André Veríssimo <andre.verissimo at tecnico.ulisboa.pt>

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("glmSparseNet")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("glmSparseNet")

 

HTML	R Script	Breast survival dataset using network from STRING DB
HTML	R Script	Example for Classification -- Breast Invasive Carcinoma
HTML	R Script	Example for Survival Data -- Breast Invasive Carcinoma
HTML	R Script	Example for Survival Data -- Prostate Adenocarcinoma
HTML	R Script	Example for Survival Data -- Skin Melanoma
PDF		Reference Manual
Text		NEWS

Details

biocViews	Classification, DimensionReduction, GraphAndNetwork, Network, Regression, Software, StatisticalMethod, Survival
Version	1.6.0
In Bioconductor since	BioC 3.8 (R-3.5) (2 years)
License	GPL (>=3)
Depends	R (>= 3.5), Matrix, MultiAssayExperiment, glmnet
Imports	SummarizedExperiment, STRINGdb, biomaRt, futile.logger, sparsebn, sparsebnUtils, forcats, dplyr, readr, ggplot2, survminer, reshape2, stats, stringr, rlang, parallel, methods, loose.rock (>= 1.0.12)
LinkingTo
Suggests	testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils
SystemRequirements
Enhances
URL	https://www.github.com/sysbiomed/glmSparseNet
BugReports	https://www.github.com/sysbiomed/glmSparseNet/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	glmSparseNet_1.6.0.tar.gz
Windows Binary	glmSparseNet_1.6.0.zip
macOS 10.13 (High Sierra)	glmSparseNet_1.6.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/glmSparseNet
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/glmSparseNet
Package Short Url	https://bioconductor.org/packages/glmSparseNet/
Package Downloads Report	Download Stats