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FAMT (version 2.6)

Factor Analysis for Multiple Testing (FAMT) : Simultaneous Tests under Dependence in High-Dimensional Data

Description

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.

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Version

Install

install.packages('FAMT')

Monthly Downloads

248

Version

2.6

License

GPL (>= 2)

Maintainer

Last Published

May 9th, 2022

Functions in FAMT (2.6)

expression

Gene expressions data frame
modelFAMT

The FAMT complete multiple testing procedure
annotations

Gene annotations data frame
FAMT-package

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

Create a 'FAMTdata' object from an expression, covariates and annotations dataset
summaryFAMT

Summary of a FAMTdata or a FAMTmodel
covariates

Covariates data frame
raw.pvalues

Calculation of classical multiple testing statistics and p-values
residualsFAMT

Calculation of residual under null hypothesis
defacto

FAMT factors description
pi0FAMT

Estimation of the Proportion of True Null Hypotheses
emfa

Factor Analysis model adjustment with the EM algorithm
nbfactors

Estimation of the optimal number of factors of the FA model