recipe bioconductor-nnnorm

Spatial and intensity based normalization of cDNA microarray data based on robust neural nets

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/nnNorm.html

License:

LGPL

Recipe:

/bioconductor-nnnorm/meta.yaml

Links:

biotools: nnnorm, doi: 10.1093/bioinformatics/bti397

This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting.

package bioconductor-nnnorm

(downloads) docker_bioconductor-nnnorm

versions:
2.66.0-0,  2.64.0-0,  2.62.0-0,  2.58.0-0,  2.56.0-0,  2.54.0-1,  2.54.0-0,  2.52.0-0,  2.50.0-0, 

2.66.0-0,  2.64.0-0,  2.62.0-0,  2.58.0-0,  2.56.0-0,  2.54.0-1,  2.54.0-0,  2.52.0-0,  2.50.0-0,  2.48.0-1,  2.48.0-0,  2.46.0-0,  2.44.0-0,  2.42.0-0,  2.40.0-0

depends bioconductor-marray:

>=1.80.0,<1.81.0

depends r-base:

>=4.3,<4.4.0a0

depends r-nnet:

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

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-nnnorm

and update with::

   mamba update bioconductor-nnnorm

To create a new environment, run:

mamba create --name myenvname bioconductor-nnnorm

with myenvname being 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-nnnorm:<tag>

(see `bioconductor-nnnorm/tags`_ for valid values for ``<tag>``)

Download stats

Link to this page

Render an install-with-bioconda badge with the following MarkDown:

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