- Home
- Bioconductor 3.0
- Software Packages
- biomvRCNS
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("biomvRCNS")
In most cases, you don't need to download the package archive at all.
biomvRCNS
Copy Number study and Segmentation for multivariate biological data
Bioconductor version: 3.0
In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing.
Author: Yang Du
Maintainer: Yang Du <yang.du at uni-rostock.de>
Citation (from within R,
enter citation("biomvRCNS")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("biomvRCNS")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("biomvRCNS")
| R Script | biomvRCNS package introduction | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | CopyNumberVariation, Genetics, Microarray, Sequencing, Software, Visualization, aCGH |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL (>= 2) |
| Depends | IRanges, GenomicRanges, Gviz |
| Imports | methods, mvtnorm |
| LinkingTo | |
| Suggests | cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene |
| 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 | biomvRCNS_1.6.0.tar.gz |
| Windows Binary | biomvRCNS_1.6.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | biomvRCNS_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | biomvRCNS_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
Post questions about Bioconductor packages to our mailing lists. Read the posting guide before posting!
Contact us: support.bioconductor.org
Hosting provided by Fred Hutchinson Cancer Research Center
Copyright © 2003 - 2015, Bioconductor
