glmSparseNet

Network Centrality Metrics for Elastic-Net Regularized Models

Bioconductor version: Release (3.17)

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>

Citation (from within R, enter citation("glmSparseNet")):

Installation

To install this package, start R (version "4.3") and enter:

if (!require("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")

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	HTML	R Script
Separate 2 groups in Cox regression	HTML	R Script
Reference Manual	PDF
NEWS	Text

Details

biocViews	Classification, DimensionReduction, GraphAndNetwork, Network, Regression, Software, StatisticalMethod, Survival
Version	1.18.0
In Bioconductor since	BioC 3.8 (R-3.5) (5 years)
License	GPL-3
Depends	R (>= 4.1), Matrix, MultiAssayExperiment, glmnet
Imports	SummarizedExperiment, biomaRt, futile.logger, futile.options, forcats, utils, dplyr, glue, readr, digest, httr, ggplot2, survminer, reshape2, stringr, parallel, methods
Linking To
Suggests	testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils
System Requirements
Enhances
URL	https://www.github.com/sysbiomed/glmSparseNet
Bug Reports	https://www.github.com/sysbiomed/glmSparseNet/issues

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Build Report

Package Archives

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

Source Package	glmSparseNet_1.18.0.tar.gz
Windows Binary	glmSparseNet_1.18.0.zip (64-bit only)
macOS Binary (x86_64)	glmSparseNet_1.18.0.tgz
macOS Binary (arm64)	glmSparseNet_1.18.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/glmSparseNet
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/glmSparseNet
Bioc Package Browser	https://code.bioconductor.org/browse/glmSparseNet/
Package Short Url	https://bioconductor.org/packages/glmSparseNet/
Package Downloads Report	Download Stats