A general framework for multiple testing dependence

By Jeffrey T. Leek, John D. Storey. 2008

393 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

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Citations in the corpus

Sufficient Forecasting Using Factor Models (2015, background): “…such as portfolio management (Fama and French, 1992; Carhart, 1997; Fama and French, 2015; Hou et al., 2015), large-scale multiple testing (Leek and Storey, 2008; Fan et al., 2012), high-dimensional covariance matrix estimation (Fan et al., 2008, 2013), and forecasting using many…”
Landscape of X chromosome inactivation across human tissues (2016, positive): “…were adjusted for age, three principal components inferred from genotype data using EIGENSTRAT 42 , sample ischemic time, surrogate variables 43,44 built using the sva R package 45 , and the cause of death classified into five categories based on the 4-point Hardy scale (Supplementary Methods).”
A Factor Model Approach to Multiple Testing Under Dependence (2009, background): “Leek and Storey (2007, 2008) also developed a multiple testing procedure exploiting the idea of borrowing information that is shared by all the variables.”
svaseq: removing batch effects and other unwanted noise from sequencing data (2014, methodology): “Unsupervised svaseq does not use the control probes but avoids some of these difficulties by iteratively identifying probes associated with group but not associated with batch (5) (Figure 7).”
Bi-cross-validation for factor analysis (2015, methodology): “The LEAPP algorithm used parallel analysis as implemented in the SVA package of [37].”
Using control genes to correct for unwanted variation in microarray data. (2012, methodology): “One of the most well-known methods that directly address this problem is SVA (Leek and Storey, 2007, 2008).”
Covariate powered cross‐weighted multiple testing (2017, background): “Once they are a matter of fact, it is sometimes possible to remove them by mapping the data to a new set of properly “normalized” and “batch-corrected” variables (Leek and Storey, 2008; Stegle et al., 2010; Wang et al., 2017).”
Speeding up Permutation Testing in Neuroimaging (2013, methodology): “Bonferroni versus true FWER threshold.”
Large Covariance Estimation by Thresholding Principal Orthogonal Complements (2011, neutral): “Controlling the false discovery rate in largescale hypothesis testing based on correlated test statistics is an important and challenging problem in statistics (Leek and Storey, 2008; Efron, 2010; Fan, et al., 2012).”