To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Rcade")
In most cases, you don't need to download the package archive at all.
Rcade
R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data.
Bioconductor version: 2.14
Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF.
Author: Jonathan Cairns
Maintainer: Jonathan Cairns <jmcairns200 at gmail.com>
Citation (from within R,
enter citation("Rcade")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("Rcade")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("Rcade")
| R Script | Rcade Vignette | |
| Reference Manual | ||
| Text | README | |
| Text | NEWS |
Details
| biocViews | ChIPSeq, DifferentialExpression, GeneExpression, Genetics, Sequencing, Software, Transcription |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.11 (R-2.15) |
| License | GPL-2 |
| Depends | R (>= 2.14.0), methods, GenomicRanges, baySeq, Rsamtools |
| Imports | graphics, IRanges, rgl |
| Suggests | limma, biomaRt, RUnit, BiocGenerics, BiocStyle |
| 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 | Rcade_1.6.0.tar.gz |
| Windows Binary | Rcade_1.6.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | Rcade_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | Rcade_1.6.0.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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