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 |
| 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 |
| CRAN checks: |
leapp results |
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