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This is the development version of snm; for the stable release version, see snm.

Supervised Normalization of Microarrays

Bioconductor version: Development (3.20)

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>

Maintainer: John D. Storey <jstorey at>

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


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


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

snm Tutorial PDF R Script
Reference Manual PDF


biocViews DifferentialExpression, ExonArray, GeneExpression, Microarray, MultiChannel, MultipleComparison, OneChannel, Preprocessing, QualityControl, Software, Transcription, TwoChannel
Version 1.53.0
In Bioconductor since BioC 2.8 (R-2.13) (13.5 years)
License LGPL
Depends R (>= 2.12.0)
Imports corpcor, lme4 (>= 1.0), splines
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Follow Installation instructions to use this package in your R session.

Source Package snm_1.53.0.tar.gz
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macOS Binary (arm64) snm_1.53.0.tgz
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