leapp: Latent Effect Adjustment After Primary Projection

These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.

Version: 1.3
Depends: R (≥ 3.1.1), sva, MASS, corpcor
Published: 2022-06-19
DOI: 10.32614/CRAN.package.leapp
Author: Yunting Sun , Nancy R.Zhang, Art B.Owen
Maintainer: Yunting Sun <yunting.sun at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
In views: Omics
CRAN checks: leapp results


Reference manual: leapp.pdf


Package source: leapp_1.3.tar.gz
Windows binaries: r-devel: leapp_1.3.zip, r-release: leapp_1.3.zip, r-oldrel: leapp_1.3.zip
macOS binaries: r-release (arm64): leapp_1.3.tgz, r-oldrel (arm64): leapp_1.3.tgz, r-release (x86_64): leapp_1.3.tgz, r-oldrel (x86_64): leapp_1.3.tgz
Old sources: leapp archive


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