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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("copynumber")
In most cases, you don't need to download the package archive at all.
copynumber
Segmentation of single- and multi-track copy number data by penalized least squares regression.
Bioconductor version: 3.0
Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results.
Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde.
Maintainer: Gro Nilsen <gronilse at ifi.uio.no>
Citation (from within R,
enter citation("copynumber")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("copynumber")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("copynumber")
| R Script | copynumber.pdf | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | CopyNumberVariation, Genetics, SNP, Software, Visualization, aCGH |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | Artistic-2.0 |
| Depends | R (>= 2.10), BiocGenerics |
| Imports | S4Vectors, IRanges, GenomicRanges |
| 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 | copynumber_1.6.0.tar.gz |
| Windows Binary | copynumber_1.6.0.zip |
| Mac OS X 10.6 (Snow Leopard) | copynumber_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | copynumber_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
- 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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