snm

Supervised Normalization of Microarrays

Bioconductor version: Development (3.0)

SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

Author: Brig Mecham and John D. Storey <jstorey at princeton.edu>

Maintainer: John D. Storey <jstorey at princeton.edu>

To install this package, start R and enter:

    source("http://bioconductor.org/biocLite.R")
    biocLite("snm")

To cite this package in a publication, start R and enter:

    citation("snm")

Documentation

PDF R Script snm Tutorial
PDF   Reference Manual
Text   NEWS

Details

biocViews DifferentialExpression, ExonArray, GeneExpression, Microarray, MultiChannel, MultipleComparison, OneChannel, Preprocessing, QualityControl, Software, Transcription, TwoChannel
Version 1.13.0
In Bioconductor since BioC 2.8 (R-2.13)
License LGPL
Depends R (>= 2.12.0)
Imports corpcor, lme4 (>= 1.0), splines
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Package Downloads

Package Source snm_1.13.0.tar.gz
Windows Binary snm_1.13.0.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) snm_1.13.0.tgz
Mac OS X 10.9 (Mavericks)
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