Package: snm 1.55.0

John D. Storey

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.

Authors:Brig Mecham and John D. Storey <[email protected]>

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snm.pdf |snm.html
snm/json (API)
NEWS

# Install 'snm' in R:
install.packages('snm', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On BioConductor:snm-1.55.0(bioc 3.21)snm-1.54.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

microarrayonechanneltwochannelmultichanneldifferentialexpressionexonarraygeneexpressiontranscriptionmultiplecomparisonpreprocessingqualitycontrol

4.38 score 60 scripts 316 downloads 2 mentions 5 exports 11 dependencies

Last updated 2 months agofrom:1e313ea37a. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 14 2024
R-4.5-winNOTEDec 14 2024
R-4.5-linuxNOTEDec 14 2024
R-4.4-winNOTEDec 14 2024
R-4.4-macNOTEDec 14 2024
R-4.3-winNOTEDec 14 2024
R-4.3-macNOTEDec 14 2024

Exports:sim.doubleChannelsim.preProcessedsim.refDesignsim.singleChannelsnm

Dependencies:bootcorpcorlatticelme4MASSMatrixminqanlmenloptrRcppRcppEigen

snm Tutorial

Rendered fromsnm.Rnwusingutils::Sweaveon Dec 14 2024.

Last update: 2013-11-01
Started: 2013-11-01