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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("virtualArray")
In most cases, you don't need to download the package archive at all.
virtualArray
This is the development version of virtualArray; for the stable release version, see virtualArray.
Build virtual array from different microarray platforms
Bioconductor version: Development (3.0)
This package permits the user to combine raw data of different microarray platforms into one virtual array. It consists of several functions that act subsequently in a semi-automatic way. Doing as much of the data combination and letting the user concentrate on analysing the resulting virtual array.
Author: Andreas Heider
Maintainer: Andreas Heider <aheider at trm.uni-leipzig.de>
Citation (from within R,
enter citation("virtualArray")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("virtualArray")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("virtualArray")
| R Script | virtualArray Vignette | |
| Reference Manual |
Details
| biocViews | DataImport, Microarray, MultipleComparison, OneChannel, Preprocessing, Software |
| Version | 1.9.0 |
| In Bioconductor since | BioC 2.10 (R-2.15) |
| License | GPL-3 |
| Depends | R (>= 2.15.0), BiocGenerics, methods, plyr, preprocessCore |
| Imports | affy, affyPLM, AnnotationDbi, Biobase, gcrma, GEOquery, graphics, methods, reshape2, stats, utils, tseries, outliers |
| Suggests | affydata, plier, limma, lumi, org.Hs.eg.db |
| 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 | virtualArray_1.9.0.tar.gz |
| Windows Binary | |
| Mac OS X 10.6 (Snow Leopard) | virtualArray_1.9.0.tgz |
| Mac OS X 10.9 (Mavericks) | |
| 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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