-
recipe
bioconductor-vsn¶ The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to “normalized log-ratios”. However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.
- Homepage
- License
Artistic-2.0
- Recipe
- Links
biotools: vsn
-
package
bioconductor-vsn¶ 
- Versions
3.50.0-0, 3.48.1-0, 3.46.0-0, 3.44.0-0, 3.38.0-1
- Depends bioconductor-affy
>=1.60.0,<1.61.0
- Depends bioconductor-biobase
>=2.42.0,<2.43.0
- Depends bioconductor-limma
>=3.38.0,<3.39.0
- Depends libgcc-ng
>=7.3.0
- Depends r-base
>=3.5.1,<3.5.2.0a0
- Depends r-ggplot2
- Depends r-lattice
- Requirements
Installation
With an activated Bioconda channel (see 2. Set up channels), install with:
conda install bioconductor-vsn
and update with:
conda update bioconductor-vsn
or use the docker container:
docker pull quay.io/biocontainers/bioconductor-vsn:<tag>
(see bioconductor-vsn/tags for valid values for
<tag>)
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/bioconductor-vsn/README.html)