The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for highthroughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated pvalues. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
Package details 


Author  David Causeur, Chloe Friguet, Magalie HoueeBigot, Maela Kloareg 
Maintainer  David Causeur <[email protected]> 
License  GPL (>= 2) 
Version  2.5 
URL  http://famt.free.fr/ 
Package repository  View on CRAN 
Installation 
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