This is the development version of geva; for the stable release version, see geva.
Gene Expression Variation Analysis (GEVA)Bioconductor version: Development (3.22)
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] ORCID: 0000-0002-6246-4658 , Murilo Zanini David [ctb], Bruno César Feltes [ctb] ORCID: 0000-0002-2825-8295 , Marcio Dorn [ctb] ORCID: 0000-0001-8534-3480
Maintainer: Itamar José Guimarães Nunes <nunesijg at gmail.com>
Citation (from within R, entercitation("geva")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("geva")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("geva")Follow Installation instructions to use this package in your R session.
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