netboost
Network Analysis Supported by Boosting
Bioconductor version: 3.24 · Package version: 2.21.5
Boosting-supported network analysis for high-dimensional omics data, implementing a three-step dimension reduction technique. A filter combined with the topological overlap measure first identifies the essential edges of the feature network, sparse hierarchical clustering then groups the selected features into modules, and each module is finally summarised by its first principal components. Subsequent analyses are carried out on these low-dimensional module signals instead of the original data, which makes the method well suited to epigenetics, metabolomics, transcriptomics, and other omics studies. The method is described in Schlosser et al. (2021) <doi:10.1109/TCBB.2020.2983010>.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("netboost") Details
| Maintainer | Pascal Schlosser <pascal.schlosser@uniklinik-freiburg.de> |
| Author | Pascal Schlosser [aut, cre] (ORCID: <https://orcid.org/0000-0002-8460-0462>), Jochen Knaus [aut, ctb], Alex Waterhölter [aut, ctb] |
| License | GPL-3 |
| URL | https://bioconductor.org/packages/release/bioc/html/netboost.html |
| Bug Reports | mailto:pascal.schlosser@uniklinik-freiburg.de |
| Downloads rank | 349 |
| Source branch | devel |
| biocViews | BiomedicalInformatics, Clustering, DimensionReduction, Epigenetics, GraphAndNetwork, Metabolomics, Network, Software, StatisticalMethod, Transcriptomics |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | netboost_2.21.5.tar.gz |
| Windows binary (x86_64) | netboost_2.21.5.zip |
| macOS binary (arm64) | netboost_2.21.5.tgz |
| macOS binary (x86_64) | netboost_2.21.5.tgz |
Dependencies
Depends: R (>= 4.0.0)
Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods
LinkingTo: Rcpp, RcppParallel