Sage-Bionetworks/snm: Supervised Normalization of Microarrays

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.

Getting started

Package details

AuthorBrig Mecham <> and John D. Storey <>
Bioconductor views DifferentialExpression ExonArray GeneExpression Microarray MultiChannel MultipleComparisons OneChannel Preprocessing QualityControl Transcription TwoChannel
MaintainerBrig Mecham <>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
Sage-Bionetworks/snm documentation built on May 9, 2019, 12:14 p.m.