A Factor Model Approach to Multiple Testing Under Dependence

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svaseq: removing batch effects and other unwanted noise from sequencing data (2014, methodology): “For step two of the sva algorithm, estimating latent factors only associated with dependence, it has been proposed to use factor analysis [12], independent components analysis [13], and principal components analysis [14].”
PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS (2014, methodology): “In calculating the false-discovery proportion for large-scale hypothesis testing, one needs to adjust accurately the common dependence via subtracting it from the data in (1.1) (Leek and Storey, 2008; Friguet et al., 2009; Efron, 2010; Desai and Storey, 2012; Fan et al., 2012).”
Estimation of False Discovery Proportion with Unknown Dependence (2013, methodology): “enite matrix of 1 + 2 by applying the algorithm of Higham (1988). Finally the covariance matrix u is set as 0:5 3. We compare our POET-PFA method with other methods such as Efron (2007)’s method and Friguet, et al (2009)’s method. Comparison with the benchmark with known covariance. We rst compare t”