FAMT: 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.

References in zbMATH (referenced in 18 articles , 1 standard article )

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  1. Zhao, Haibing: Estimating false discovery proportion in multiple comparison under dependency (2017)
  2. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  3. Bodnar, Taras; Reiß, Markus: Exact and asymptotic tests on a factor model in low and large dimensions with applications (2016)
  4. Delattre, Sylvain; Roquain, Etienne: On empirical distribution function of high-dimensional Gaussian vector components with an application to multiple testing (2016)
  5. Fan, Jianqing; Liao, Yuan; Wang, Weichen: Projected principal component analysis in factor models (2016)
  6. Jessie Jeng, X.: Detecting weak signals in high dimensions (2016)
  7. Perthame, Émeline; Friguet, Chloé; Causeur, David: Stability of feature selection in classification issues for high-dimensional correlated data (2016)
  8. Sheu, Ching-Fan; Perthame, Émeline; Lee, Yuh-Shiow; Causeur, David: Accounting for time dependence in large-scale multiple testing of event-related potential data (2016)
  9. Delattre, Sylvain; Roquain, Etienne: New procedures controlling the false discovery proportion via Romano-Wolf’s heuristic (2015)
  10. Zhao, Haibing; Peddada, Shyamal D.; Cui, Xinping: Mixed directional false discovery rate control in multiple pairwise comparisons using weighted $p$-values (2015)
  11. Desai, Keyur H.; Storey, John D.: Cross-dimensional inference of dependent high-dimensional data (2012)
  12. Fan, Jianqing; Han, Xu; Gu, Weijie: Estimating false discovery proportion under arbitrary covariance dependence (2012)
  13. Friguet, Chloé: A general approach to account for dependence in large-scale multiple testing (2012)
  14. Kuan, Pei Fen; Chiang, Derek Y.: Integrating prior knowledge in multiple testing under dependence with applications to detecting differential DNA methylation (2012)
  15. Sun, Yunting; Zhang, Nancy R.; Owen, Art B.: Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data (2012)
  16. Leek, Jeffrey T.; Storey, John D.: The joint null criterion for multiple hypothesis tests (2011)
  17. Causeur, D.; Kloareg, M.; Friguet, C.: Control of the FWER in multiple testing under dependence (2009)
  18. Friguet, Chloé; Kloareg, Maela; Causeur, David: A factor model approach to multiple testing under dependence (2009)