Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
| Version: | 0.1.3 |
| Depends: | R (≥ 3.1.0), sva, isva, RSpectra |
| Imports: | Rcpp, stats, utils |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2017-05-28 |
| DOI: | 10.32614/CRAN.package.SmartSVA |
| Author: | Jun Chen, Ehsan Behnam |
| Maintainer: | Jun Chen <Chen.Jun2 at mayo.edu> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| CRAN checks: | SmartSVA results |
| Reference manual: | SmartSVA.html , SmartSVA.pdf |
| Package source: | SmartSVA_0.1.3.tar.gz |
| Windows binaries: | r-devel: SmartSVA_0.1.3.zip, r-release: SmartSVA_0.1.3.zip, r-oldrel: SmartSVA_0.1.3.zip |
| macOS binaries: | r-release (arm64): SmartSVA_0.1.3.tgz, r-oldrel (arm64): SmartSVA_0.1.3.tgz, r-release (x86_64): SmartSVA_0.1.3.tgz, r-oldrel (x86_64): SmartSVA_0.1.3.tgz |
| Old sources: | SmartSVA archive |
| Reverse imports: | MEAL, omicRexposome |
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