geva
Gene Expression Variation Analysis (GEVA)
Bioconductor version: Release (3.19)
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("geva")):
Installation
To install this package, start R (version "4.4") 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")| GEVA | R Script | |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, Software, SystemsBiology, Transcriptomics |
| Version | 1.12.0 |
| In Bioconductor since | BioC 3.13 (R-4.1) (3.5 years) |
| License | LGPL-3 |
| Depends | R (>= 4.1) |
| Imports | grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats |
| System Requirements | |
| URL | https://github.com/sbcblab/geva |
See More
| Suggests | devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0) |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | geva_1.12.0.tar.gz |
| Windows Binary (x86_64) | geva_1.12.0.zip (64-bit only) |
| macOS Binary (x86_64) | geva_1.12.0.tgz |
| macOS Binary (arm64) | geva_1.12.0.tgz |
| 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 |