Package: snm 1.55.0
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:
snm_1.55.0.tar.gz
snm_1.55.0.zip(r-4.5)snm_1.55.0.zip(r-4.4)snm_1.55.0.zip(r-4.3)
snm_1.55.0.tgz(r-4.4-any)snm_1.55.0.tgz(r-4.3-any)
snm_1.55.0.tar.gz(r-4.5-noble)snm_1.55.0.tar.gz(r-4.4-noble)
snm_1.55.0.tgz(r-4.4-emscripten)snm_1.55.0.tgz(r-4.3-emscripten)
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')) |
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
Last updated 23 days agofrom:1e313ea37a. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 14 2024 |
R-4.5-win | NOTE | Nov 14 2024 |
R-4.5-linux | NOTE | Nov 14 2024 |
R-4.4-win | NOTE | Nov 14 2024 |
R-4.4-mac | NOTE | Nov 14 2024 |
R-4.3-win | NOTE | Nov 14 2024 |
R-4.3-mac | NOTE | Nov 14 2024 |
Exports:sim.doubleChannelsim.preProcessedsim.refDesignsim.singleChannelsnm
Dependencies:bootcorpcorlatticelme4MASSMatrixminqanlmenloptrRcppRcppEigen
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Simulated data for a double channel microarray experiment. | sim.doubleChannel |
Simulate data from a microarray experiment without any intensity-dependent effects. | sim.preProcessed |
Simulates data from a two-color microarray experiment using a reference design. | sim.refDesign |
Simulate data from a single channel microarray experiment | sim.singleChannel |
Perform a supervised normalization of microarray data | snm |
Extract fitted values from an snm object | fitted.snm snm.fitted |
Display plots for an snm object | plot.snm snm.plot |
Display summary information for an snm object | snm.summary summary.snm |