GeneAccord

DOI: 10.18129/B9.bioc.GeneAccord    

This package is for version 3.10 of Bioconductor; for the stable, up-to-date release version, see GeneAccord.

Detection of clonally exclusive gene or pathway pairs in a cohort of cancer patients

Bioconductor version: 3.10

A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations.

Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel

Maintainer: Ariane L. Moore <ariane.moore at bsse.ethz.ch>

Installation

To install this package, start R (version "3.6") and enter:

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

BiocManager::install("GeneAccord")

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("GeneAccord")

 

HTML	R Script	GeneAccord
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	BiomedicalInformatics, FeatureExtraction, FunctionalGenomics, GeneticVariability, Genetics, GenomicVariation, MathematicalBiology, Pathways, PatternLogic, Software, SomaticMutation, SystemsBiology
Version	1.4.0
In Bioconductor since	BioC 3.8 (R-3.5) (1.5 years)
License	file LICENSE
Depends	R (>= 3.5)
Imports	biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools, ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats, tibble, utils
LinkingTo
Suggests	assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat
SystemRequirements
Enhances
URL	https://github.com/cbg-ethz/GeneAccord
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	GeneAccord_1.4.0.tar.gz
Windows Binary	GeneAccord_1.4.0.zip
Mac OS X 10.11 (El Capitan)	GeneAccord_1.4.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/GeneAccord
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GeneAccord
Package Short Url	https://bioconductor.org/packages/GeneAccord/
Package Downloads Report	Download Stats