Factor Analysis for Multiple Testing (FAMT) : Simultaneous Tests under Dependence in High-Dimensional Data
Create a 'FAMTdata' object from an expression, covariates and annotati...
FAMT factors description
Factor Analysis model adjustment with the EM algorithm
Factor Analysis for Multiple Testing (FAMT) : simultaneous tests under...
The FAMT complete multiple testing procedure
Estimation of the optimal number of factors of the FA model
Estimation of the Proportion of True Null Hypotheses
Calculation of classical multiple testing statistics and p-values
Calculation of residual under null hypothesis
Summary of a FAMTdata or a FAMTmodel
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput 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 p-values. 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.