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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GOFunction")
In most cases, you don't need to download the package archive at all.
GOFunction
GO-function: deriving biologcially relevant functions from statistically significant functions
Bioconductor version: 3.0
The GO-function package provides a tool to address the redundancy that result from the GO structure or multiple annotation genes and derive biologically relevant functions from the statistically significant functions based on some intuitive assumption and statistical testing.
Author: Jing Wang <jingwang.uestc at gmail.com>
Maintainer: Jing Wang <jingwang.uestc at gmail.com>
Citation (from within R,
enter citation("GOFunction")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("GOFunction")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GOFunction")
| R Script | GO-function | |
| Reference Manual |
Details
| biocViews | GO, GeneSetEnrichment, Microarray, Pathways, Software |
| Version | 1.14.0 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | GPL (>= 2) |
| Depends | R (>= 2.11.0), methods, Biobase(>= 2.8.0), graph(>= 1.26.0), Rgraphviz(>= 1.26.0), GO.db(>= 2.4.1), AnnotationDbi(>= 1.10.2), SparseM (>= 0.85) |
| Imports | methods, Biobase, graph, Rgraphviz, GO.db, AnnotationDbi, SparseM |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | GOFunction_1.14.0.tar.gz |
| Windows Binary | GOFunction_1.14.0.zip |
| Mac OS X 10.6 (Snow Leopard) | GOFunction_1.14.0.tgz |
| Mac OS X 10.9 (Mavericks) | GOFunction_1.14.0.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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