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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RUVnormalizeData")
In most cases, you don't need to download the package archive at all.
RUVnormalizeData
Gender data for the RUVnormalize package
Bioconductor version: 3.0
Microarray gene expression data from the study of Vawter et al., 2004.
Author: Laurent Jacob
Maintainer: Laurent Jacob <laurent.jacob at univ-lyon1.fr>
Citation (from within R,
enter citation("RUVnormalizeData")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("RUVnormalizeData")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("RUVnormalizeData")
| R Script | RUVnormalizeData | |
| Reference Manual |
Details
| biocViews | ExperimentData, ExpressionData |
| Version | 1.0.0 |
| License | GPL-3 |
| Depends | R (>= 2.10.0), Biobase |
| Imports | |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | RUVnormalize |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | RUVnormalizeData_1.0.0.tar.gz |
| Windows Binary | RUVnormalizeData_1.0.0.zip |
| Mac OS X 10.6 (Snow Leopard) | RUVnormalizeData_1.0.0.tgz |
| Mac OS X 10.9 (Mavericks) | RUVnormalizeData_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
- 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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