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snm

Supervised Normalization of Microarrays

Bioconductor version: 3.23 · Package version: 1.60.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("snm")

Details

MaintainerJohn D. Storey <jstorey@princeton.edu>
AuthorBrig Mecham and John D. Storey <jstorey@princeton.edu>
LicenseLGPL
Downloads rank508
Source branchRELEASE_3_23
biocViewsDifferentialExpression, ExonArray, GeneExpression, Microarray, MultiChannel, MultipleComparison, OneChannel, Preprocessing, QualityControl, Software, Transcription, TwoChannel

Documentation

Download

Follow the installation instructions to use this package in your R session.

Source packagesnm_1.60.0.tar.gz
Windows binary (x86_64)snm_1.60.0.zip
macOS binary (arm64)snm_1.60.0.tgz
macOS binary (x86_64)snm_1.60.0.tgz
Dependencies

Depends: R (>= 2.12.0)

Imports: corpcor, lme4 (>= 1.0), splines

Reverse dependencies

Imports Me (1): ExpressionNormalizationWorkflow