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.
|Author||Yunting Sun <email@example.com> , Nancy R.Zhang <firstname.lastname@example.org>, Art B.Owen <email@example.com>|
|Date of publication||2014-07-22 08:52:54|
|Maintainer||Yunting Sun <firstname.lastname@example.org>|
|License||GPL (>= 2)|
AlternateSVD: Alternating singular value decomposition
FindAUC: Compute the area under the ROC curve (AUC)
FindFpr: Compute the false positive rate at given sizes of retrieved...
FindPrec: compute the precision at given sizes of retrieved genes
FindRec: compute the recall at given sizes of retrieved genes
FindTpr: compute the true positive rate at given sizes of retrieved...
IPOD: Iterative penalized outlier detection algorithm
IPODFUN: compute the iterative penalized outlier detection given the...
leapp: latent effect adjustment after primary projection
leapp-package: latent effect adjustment after primary projection
Pvalue: Calculate statistics and p-values
ridge: Outlier detection with a ridge penalty
ROCplot: plot ROC curve
simdat: Simulated gene expression data affected by a group variable...