To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("neaGUI")
In most cases, you don't need to download the package archive at all.
neaGUI
An R package to perform the network enrichment analysis (NEA).
Bioconductor version: 3.0
neaGUI is an easy to use R package developed to perform the network enrichment analysis (NEA) proposed by Alexeyenko et al. (2012). The NEA method extends the overlap statistics in GSEA to network links between genes in the experimental set and those in the functional categories by exploiting biological information in terms of gene interaction network. The neaGUI requires the following R packages: tcltk, KEGG.db, GO.db, reactome.db, org.Hs.eg.db, AnnotationDbi, and hwriter.
Author: Setia Pramana, Woojoo Lee, Yudi Pawitan
Maintainer: Setia Pramana <setia.pramana at ki.se>
Citation (from within R,
enter citation("neaGUI")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("neaGUI")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("neaGUI")
| R Script | neaGUI Vignette | |
| Reference Manual |
Details
| biocViews | DifferentialExpression, GUI, Microarray, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL-2 |
| Depends | tcltk |
| Imports | hwriter |
| LinkingTo | |
| Suggests | AnnotationDbi, org.Hs.eg.db, KEGG.db, GO.db, reactome.db, RUnit, GOstats, hwriter |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | EnrichmentBrowser |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | neaGUI_1.4.0.tar.gz |
| Windows Binary | neaGUI_1.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | neaGUI_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | neaGUI_1.4.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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