To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CompGO")
In most cases, you don't need to download the package archive at all.
CompGO
An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation
Bioconductor version: 2.14
This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots.
Author: Sam Bassett [aut], Ash Waardenberg [aut, cre]
Maintainer: Ash Waardenberg <A.Waardenberg at victorchang.edu.au>
Citation (from within R,
enter citation("CompGO")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("CompGO")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("CompGO")
| R Script | Introduction | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | GO, GeneSetEnrichment, MultipleComparison, Software, Visualization |
| Version | 1.0.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | GPL-2 |
| Depends | RDAVIDWebService |
| Imports | rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene |
| Suggests | |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | CompGO_1.0.0.tar.gz |
| Windows Binary | CompGO_1.0.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | CompGO_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | CompGO_1.0.0.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
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