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.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
- Delattre, Sylvain; Roquain, Etienne: On empirical distribution function of high-dimensional Gaussian vector components with an application to multiple testing (2016)
- Perthame, Émeline; Friguet, Chloé; Causeur, David: Stability of feature selection in classification issues for high-dimensional correlated data (2016)
- Sheu, Ching-Fan; Perthame, Émeline; Lee, Yuh-Shiow; Causeur, David: Accounting for time dependence in large-scale multiple testing of event-related potential data (2016)
- Sun, Yunting; Zhang, Nancy R.; Owen, Art B.: Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data (2012)