geva

DOI: 10.18129/B9.bioc.geva  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see geva.

Gene Expression Variation Analysis (GEVA)

Bioconductor version: 3.17

Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors.

Author: Itamar José Guimarães Nunes [aut, cre] , Murilo Zanini David [ctb], Bruno César Feltes [ctb] , Marcio Dorn [ctb]

Maintainer: Itamar José Guimarães Nunes <nunesijg at gmail.com>

Installation

To install this package, start R (version "4.3") and enter:

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

BiocManager::install("geva")

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("geva")

 

PDF	R Script	GEVA
PDF		Reference Manual
Text		NEWS

Details

biocViews	Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, Software, SystemsBiology, Transcriptomics
Version	1.8.0
In Bioconductor since	BioC 3.13 (R-4.1) (2.5 years)
License	LGPL-3
Depends	R (>= 4.1)
Imports	grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats
LinkingTo
Suggests	devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0)
SystemRequirements
Enhances
URL	https://github.com/sbcblab/geva
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	geva_1.8.0.tar.gz
Windows Binary	geva_1.8.0.zip
macOS Binary (x86_64)	geva_1.8.0.tgz
macOS Binary (arm64)
Source Repository	git clone https://git.bioconductor.org/packages/geva
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/geva
Bioc Package Browser	https://code.bioconductor.org/browse/geva/
Package Short Url	https://bioconductor.org/packages/geva/
Package Downloads Report	Download Stats