nnNorm
Spatial and intensity based normalization of cDNA microarray data based on robust neural nets
Bioconductor version: Release (3.20)
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.
Author: Adi Laurentiu Tarca <atarca at med.wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca at med.wayne.edu>
Citation (from within R, enter
citation("nnNorm")
):
Installation
To install this package, start R (version "4.4") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("nnNorm")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("nnNorm")
nnNorm Tutorial | R Script | |
Reference Manual |
Details
biocViews | Microarray, Preprocessing, Software, TwoChannel |
Version | 2.70.0 |
In Bioconductor since | BioC 1.6 (R-2.1) or earlier (> 19.5 years) |
License | LGPL |
Depends | R (>= 2.2.0), marray |
Imports | graphics, grDevices, marray, methods, nnet, stats |
System Requirements | |
URL | http://bioinformaticsprb.med.wayne.edu/tarca/ |
See More
Suggests | |
Linking To | |
Enhances | |
Depends On Me | |
Imports Me | |
Suggests Me | |
Links To Me | |
Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
Source Package | nnNorm_2.70.0.tar.gz |
Windows Binary (x86_64) | nnNorm_2.70.0.zip |
macOS Binary (x86_64) | nnNorm_2.70.0.tgz |
macOS Binary (arm64) | nnNorm_2.70.0.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/nnNorm |
Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/nnNorm |
Bioc Package Browser | https://code.bioconductor.org/browse/nnNorm/ |
Package Short Url | https://bioconductor.org/packages/nnNorm/ |
Package Downloads Report | Download Stats |