# FAMT v2.5

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## Factor Analysis for Multiple Testing (FAMT) : simultaneous tests under dependence in high-dimensional data

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.

## Functions in FAMT

 Name Description FAMT-package Factor Analysis for Multiple Testing (FAMT) : simultaneous tests under dependence in high-dimensional data raw.pvalues Calculation of classical multiple testing statistics and p-values covariates Covariates data frame annotations Gene annotations data frame as.FAMTdata Create a 'FAMTdata' object from an expression, covariates and annotations dataset emfa Factor Analysis model adjustment with the EM algorithm pi0FAMT Estimation of the Proportion of True Null Hypotheses nbfactors Estimation of the optimal number of factors of the FA model summaryFAMT Summary of a FAMTdata or a FAMTmodel defacto FAMT factors description modelFAMT The FAMT complete multiple testing procedure residualsFAMT Calculation of residual under null hypothesis expression Gene expressions data frame No Results!