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: 2.14
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")
| R Script | snm Tutorial | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | DifferentialExpression, ExonArray, GeneExpression, Microarray, MultiChannel, MultipleComparison, OneChannel, Preprocessing, QualityControl, Software, Transcription, TwoChannel |
| Version | 1.12.0 |
| 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_1.12.0.tar.gz |
| Windows Binary | snm_1.12.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | snm_1.12.0.tgz |
| Mac OS X 10.9 (Mavericks) | snm_1.12.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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