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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

MaintainerPascal Schlosser <pascal.schlosser@uniklinik-freiburg.de>
AuthorPascal Schlosser [aut, cre] (ORCID: <https://orcid.org/0000-0002-8460-0462>), Jochen Knaus [aut, ctb], Alex Waterhölter [aut, ctb]
LicenseGPL-3
URLhttps://bioconductor.org/packages/release/bioc/html/netboost.html
Bug Reportsmailto:pascal.schlosser@uniklinik-freiburg.de
Downloads rank349
Source branchdevel
biocViewsBiomedicalInformatics, Clustering, DimensionReduction, Epigenetics, GraphAndNetwork, Metabolomics, Network, Software, StatisticalMethod, Transcriptomics

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packagenetboost_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

Suggests: knitr, rmarkdown, BiocStyle