KBoost

DOI: 10.18129/B9.bioc.KBoost    

Inference of gene regulatory networks from gene expression data

Bioconductor version: Release (3.14)

Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets.

Author: Luis F. Iglesias-Martinez [aut, cre] , Barbara de Kegel [aut], Walter Kolch [aut]

Maintainer: Luis F. Iglesias-Martinez <luis.iglesiasmartinez at ucd.ie>

Installation

To install this package, start R (version "4.1") and enter:

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

BiocManager::install("KBoost")

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("KBoost")

 

HTML	R Script	KBoost
PDF		Reference Manual
Text		NEWS

Details

biocViews	Bayesian, GeneExpression, GeneRegulation, GraphAndNetwork, Network, NetworkInference, PrincipalComponent, Regression, Software, SystemsBiology, Transcription, Transcriptomics
Version	1.2.0
In Bioconductor since	BioC 3.13 (R-4.1) (1 year)
License	GPL-2 | GPL-3
Depends	R (>= 4.1), stats, utils
Imports
LinkingTo
Suggests	knitr, rmarkdown, testthat
SystemRequirements
Enhances
URL	https://github.com/Luisiglm/KBoost
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	KBoost_1.2.0.tar.gz
Windows Binary	KBoost_1.2.0.zip
macOS 10.13 (High Sierra)	KBoost_1.2.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/KBoost
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/KBoost
Package Short Url	https://bioconductor.org/packages/KBoost/
Package Downloads Report	Download Stats
Old Source Packages for BioC 3.14	Source Archive