glmSparseNet
Network Centrality Metrics for Elastic-Net Regularized Models
Bioconductor version: 3.23 · Package version: 1.30.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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".
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("glmSparseNet")Details
| Maintainer | André Veríssimo <andre.verissimo@tecnico.ulisboa.pt> |
| Author | André Veríssimo [aut, cre] (ORCID: <https://orcid.org/0000-0002-2212-339X>), Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] |
| License | GPL-3 |
| URL | https://www.github.com/sysbiomed/glmSparseNet |
| Bug Reports | https://www.github.com/sysbiomed/glmSparseNet/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | Classification, DimensionReduction, GraphAndNetwork, Network, Regression, Software, StatisticalMethod, Survival |
| Package Short Url | https://bioconductor.org/packages/glmSparseNet/ |
Citation
From within R, enter citation("glmSparseNet"):
André Veríssimo, Susana Vinga. glmSparseNet: Network Centrality Metrics for Elastic-Net Regularized Models. doi:10.18129/B9.bioc.glmSparseNet, R package version 1.30.0, https://bioconductor.org/packages/glmSparseNet.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
- Breast survival dataset using network from STRING DB
- Example for Classification Data -- Breast Invasive Carcinoma
- Example for Survival Data -- Breast Invasive Carcinoma
- Example for Survival Data -- Prostate Adenocarcinoma
- Example for Survival Data -- Skin Melanoma
- Separate 2 groups in Cox regression
Download
Follow the installation instructions to use this package in your R session.
| Source package | glmSparseNet_1.30.0.tar.gz |
| Windows binary (x86_64) | glmSparseNet_1.30.0.zip |
| macOS binary (arm64) | glmSparseNet_1.30.0.tgz |
| macOS binary (x86_64) | glmSparseNet_1.30.0.tgz |
Dependencies
Depends: R (>= 4.3.0)
Imports: biomaRt, checkmate, dplyr, forcats, futile.logger, ggplot2, glue, httr, lifecycle, methods, parallel, readr, rlang, glmnet, Matrix, MultiAssayExperiment, SummarizedExperiment, survminer, TCGAutils, utils
Suggests: BiocStyle, curatedTCGAData, knitr, magrittr, reshape2, pROC, rmarkdown, survival, testthat, VennDiagram, withr
Reverse dependencies
Imports Me (1): priorityelasticnet