FAMT is a R package for simultaneous tests under dependence in high-dimensional data. It is developed and maintained by David Causeur, Chloé Friguet, Maela Kloareg and Magalie Houée-Bigot from Agrocampus Ouest applied mathematics department.
The method proposed in this package takes into account the impact of dependence on multiple testing procedures for high-throughput data. The common information shared by all the variables is modeled by a factor analysis structure. New test statistics for general linear contrasts are deduced, taking advantage of the common factor structure to reduce correlation and consequently the variance of error rates. This method improves the conditional FDR estimate and the overall performance of multiple testing procedure (decreasing the no-discovery proportion).
The package contains some key functions. The as.FAMTdata function creates a single R object containing the data stored in one mandatory data-frame (the 'expression' dataset), and two optional data-frames (the 'covariates' and 'annotations' datasets). The function checks the consistency of dataframes between them. With the modelFAMT function, the user can perform classical multiple testing statistics or FAMT method. The defacto function provides diagnostic plots to interpret and describe the factors.
Last modified: 2011-10-11