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 <email@example.com>|
|Date of publication||None|
|Maintainer||John D. Storey <firstname.lastname@example.org>|
fitted.snm: Extract fitted values from an snm object
plot.snm: Display plots for an snm object
sim.doubleChannel: Simulated data for a double channel microarray experiment.
sim.preProcessed: Simulate data from a microarray experiment without any...
sim.refDesign: Simulates data from a two-color microarray experiment using a...
sim.singleChannel: Simulate data from a single channel microarray experiment
snm: Perform a supervised normalization of microarray data
snm-internal: Internal snm functions.
summary.snm: Display summary information for an snm object