To install this package, start R and enter:

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

In most cases, you don't need to download the package archive at all.

snm

Supervised Normalization of Microarrays

Bioconductor version: 2.13

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>

Citation (from within R, enter citation("snm")):

Installation

To install this package, start R and enter:

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

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("snm")

 

PDF R Script snm Tutorial
PDF   Reference Manual
Text   NEWS

Details

biocViews DifferentialExpression, ExonArray, GeneExpression, Microarray, MultiChannel, MultipleComparisons, OneChannel, Preprocessing, QualityControl, Software, Transcription, TwoChannel
Version 1.10.2
In Bioconductor since BioC 2.8 (R-2.13)
License LGPL
Depends R (>= 2.12.0)
Imports corpcor, lme4 (>= 1.0), splines
Suggests
System Requirements
URL
Depends On Me
Imports Me
Suggests Me

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source snm_1.10.2.tar.gz
Windows Binary snm_1.10.2.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) snm_1.10.2.tgz
Browse/checkout source (username/password: readonly)
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