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KBoost

Inference of gene regulatory networks from gene expression data


Bioconductor version: Release (3.18)

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>

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

Installation

To install this package, start R (version "4.3") 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")
KBoost HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews Bayesian, GeneExpression, GeneRegulation, GraphAndNetwork, Network, NetworkInference, PrincipalComponent, Regression, Software, SystemsBiology, Transcription, Transcriptomics
Version 1.10.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License GPL-2 | GPL-3
Depends R (>= 4.1), stats, utils
Imports
System Requirements
URL https://github.com/Luisiglm/KBoost
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Suggests knitr, rmarkdown, testthat
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Package Archives

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

Source Package KBoost_1.10.0.tar.gz
Windows Binary KBoost_1.10.0.zip (64-bit only)
macOS Binary (x86_64) KBoost_1.10.0.tgz
macOS Binary (arm64) KBoost_1.10.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/KBoost
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/KBoost
Bioc Package Browser https://code.bioconductor.org/browse/KBoost/
Package Short Url https://bioconductor.org/packages/KBoost/
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