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snm (version 1.20.0)

Supervised Normalization of Microarrays

Description

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.

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Version

Version

1.20.0

License

LGPL

Maintainer

Last Published

February 15th, 2017

Functions in snm (1.20.0)

snm

Perform a supervised normalization of microarray data
snm.summary

Display summary information for an snm object
sim.refDesign

Simulates data from a two-color microarray experiment using a reference design.
sim.singleChannel

Simulate data from a single channel microarray experiment
snm.fitted

Extract fitted values from an snm object
sim.doubleChannel

Simulated data for a double channel microarray experiment.
sim.preProcessed

Simulate data from a microarray experiment without any intensity-dependent effects.
snm.plot

Display plots for an snm object
buildBasisFunction

Internal snm functions.