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.
|Author||David Causeur, Chloe Friguet, Magalie Houee-Bigot, Maela Kloareg|
|Date of publication||2014-01-02 15:15:13|
|Maintainer||David Causeur <David.Causeur@agrocampus-ouest.fr>|
|License||GPL (>= 2)|
annotations: Gene annotations data frame
as.FAMTdata: Create a 'FAMTdata' object from an expression, covariates and...
covariates: Covariates data frame
defacto: FAMT factors description
emfa: Factor Analysis model adjustment with the EM algorithm
expression: Gene expressions data frame
FAMT-package: Factor Analysis for Multiple Testing (FAMT) : simultaneous...
modelFAMT: The FAMT complete multiple testing procedure
nbfactors: Estimation of the optimal number of factors of the FA model
pi0FAMT: Estimation of the Proportion of True Null Hypotheses
raw.pvalues: Calculation of classical multiple testing statistics and...
residualsFAMT: Calculation of residual under null hypothesis
summaryFAMT: Summary of a FAMTdata or a FAMTmodel