geva

DOI: 10.18129/B9.bioc.geva    

Gene Expression Variation Analysis (GEVA)

Bioconductor version: Release (3.14)

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>

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

Installation

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

if (!requireNamespace("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 1.2.0
In Bioconductor since BioC 3.13 (R-4.1) (< 6 months)
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_1.2.0.tar.gz
Windows Binary geva_1.2.0.zip
macOS 10.13 (High Sierra) geva_1.2.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/geva
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/geva
Package Short Url https://bioconductor.org/packages/geva/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.14 Source Archive

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