To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("FGNet")
In most cases, you don't need to download the package archive at all.
FGNet
Functional Gene Networks derived from biological enrichment analyses
Bioconductor version: 3.0
Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO.
Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas.
Maintainer: Sara Aibar <saibar at usal.es>
Citation (from within R,
enter citation("FGNet")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("FGNet")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("FGNet")
| HTML | R Script | FGNet |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Annotation, Clustering, FunctionalGenomics, GO, GeneSetEnrichment, Network, NetworkEnrichment, Pathways, Software, Visualization |
| Version | 3.0.7 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL (>= 2) |
| Depends | R (>= 2.15) |
| Imports | igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png |
| LinkingTo | |
| Suggests | RGtk2, RCurl, RDAVIDWebService, gage, topGO, KEGGprofile, GO.db, KEGG.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi |
| SystemRequirements | |
| Enhances | |
| URL | http://www.cicancer.org |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | FGNet_3.0.7.tar.gz |
| Windows Binary | FGNet_3.0.7.zip |
| Mac OS X 10.6 (Snow Leopard) | FGNet_3.0.7.tgz |
| Mac OS X 10.9 (Mavericks) | FGNet_3.0.7.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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