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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("cummeRbund")
In most cases, you don't need to download the package archive at all.
cummeRbund
Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data.
Bioconductor version: 2.14
Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations.
Author: L. Goff, C. Trapnell, D. Kelley
Maintainer: Loyal A. Goff <lgoff at csail.mit.edu>
Citation (from within R,
enter citation("cummeRbund")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("cummeRbund")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cummeRbund")
| R Script | CummeRbund User Guide | |
| R Script | Sample cummeRbund workflow | |
| Reference Manual | ||
| Text | README | |
| Text | NEWS |
Details
| biocViews | Clustering, DataImport, DataRepresentation, DifferentialExpression, GeneExpression, Infrastructure, MultipleComparison, QualityControl, RNASeq, RNAseqData, Sequencing, Software, Visualization |
| Version | 2.6.1 |
| In Bioconductor since | BioC 2.9 (R-2.14) |
| License | Artistic-2.0 |
| Depends | R (>= 2.7.0), BiocGenerics(>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz |
| Imports | methods, plyr, BiocGenerics |
| Suggests | cluster, plyr |
| System Requirements | |
| URL | |
| Depends On Me | meshr, spliceR |
| Imports Me | |
| Suggests Me | oneChannelGUI |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | cummeRbund_2.6.1.tar.gz |
| Windows Binary | cummeRbund_2.6.1.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | cummeRbund_2.6.1.tgz |
| Mac OS X 10.9 (Mavericks) | cummeRbund_2.6.1.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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