- recipe bioconductor-vsn
Variance stabilization and calibration for microarray data
The package implements a method for normalising microarray intensities from 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.
- package bioconductor-vsn¶
- depends bioconductor-affy:
- depends bioconductor-biobase:
- depends bioconductor-limma:
- depends libblas:
- depends libgcc-ng:
- depends liblapack:
- depends r-base:
- depends r-ggplot2:
- depends r-lattice:
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install bioconductor-vsn and update with:: mamba update bioconductor-vsn
To create a new environment, run:
mamba create --name myenvname bioconductor-vsn
myenvnamebeing a reasonable name for the environment (see e.g. the mamba docs for details and further options).
Alternatively, use the docker container:
docker pull quay.io/biocontainers/bioconductor-vsn:<tag> (see `bioconductor-vsn/tags`_ for valid values for ``<tag>``)