Empirical Bayes Analysis of a Microarray Experiment

By Bradley Efron, Robert Tibshirani, John D. Storey, V. Tusher. 2001

1,921 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b719662405c578178d82971e97ab852ff3e1db74

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In Molecular Biology

Citations in the corpus

The Bayesian Two-Sample t Test (2005, background): “For Jeffreys (1961), dependence of the prior for δ on the value of σ is implicit in his assertion “from conditions of similarity, it [the mean] must depend on σ, since there is nothing in the problem except σ to give a scale for [the mean].”
Estimating the Occurrence of False Positives and False Negatives in Microarray Studies by Approximating and Partitioning the Empirical Distribution of P-values (2003, methodology): “Expressing the estimated density in terms of its two components leads immediately to the empirical Bayes’ interpretation introduced by Efronet al. (2001). For a given p-value x , the estimated lower bound for the EBP”
A Selective Overview of Variable Selection in High Dimensional Feature Space (2009, background): “See also Efron et al. (2001), Abramovich et al. (2006), Donoho and Jin (2006), and Clarke and Hall (2009).”
A review of feature selection techniques in bioinformatics (2007, methodology): “These techniques have emerged as a solid alternative to deal with the specificities of DNA microarray data, and do not depend on strong parametric assumptions [31, 87, 88, 121].”
Genome-wide methylation analysis of human colon cancer reveals similar hypo- and hypermethylation at conserved tissue-specific CpG island shores (2009, methodology): “We used a permutation test to form a null distribution for these areas and the empirical Bayes approach describe”
The optimal discovery procedure: a new approach to simultaneous significance testing (2007, neutral): “…this is quite difficult to answer in general, we have shown that an estimated ODP substantially outperforms the empirical Bayesian methods of Efron et al. (2001) and Lönnstedt and Speed (2002) when attempting to identify differentially expressed genes in DNA microarray experiments (Storey…”
Operating characteristics and extensions of the false discovery rate procedure (2002, background): “This mixture approach is pursued in detail in Efron et al. (2001), Genovese and Wasserman (2001) and Storey (2002).”
False discovery rates: a new deal (2016, background): “Efron and co-authors [3–7]; see also [8–11] for example.”
Exact calculations for false discovery proportion with application to least favorable configurations (2010, background): “For mathematical convenience, most of these results stand in “unconditional” models (also called “two-groups mixture models”; see [10, 12]), which assume that each null hypothesis is true with probability π0, independently from the other hypotheses (the case of the more traditional “conditional” mod”
The Bayes oracle and asymptotic optimality of multiple testing procedures under sparsity (2010, methodology): “The proposed mixture model for Xi is a specific example of the two-groups model, which was discussed in a wider nonparametric context, for example, in [6, 17, 19] and [24].”
Covariate‐assisted ranking and screening for large‐scale two‐sample inference (2019, methodology): “Define the local false discovery rate Lfdr (Efron et al., 2001) as Lfdr1.”
On the performance of FDR control: Constraints and a partial solution (2007, background): “and pFDR control. 1. Introduction. Since the original work of Benjamini and Hochberg [2], the False Discovery Rate (FDR) paradigm has become an attractive approach to multiple hypothesis testing (cf. [1, 2, 5, 6, 7, 8, 9, 10, 12, 15, 16, 17] and references therein). FDR is the expected value of the ”
Testing the Number of Components in Normal Mixture Regression Models (2015, background): “A finite normal mixture model provides an approach to finding differentially expressed genes by means of the posterior probability that an individual gene is non-differentially expressed (Lee et al., 2000; Efron et al., 2001).”
A general framework for multiple testing dependence (2008, methodology): “where we have replaced P̂r ( bi = 0|γi 6= 0,X,S, Ĝ(b) ) with Pr(bi 6= 0|Fi) at a quantifiable loss of information (8).”
MetaboAnalyst: a web server for metabolomic data analysis and interpretation (2009, methodology): “Typical SAM and EBAM plots are provided to assist users in choosing the best parameters and viewing the results.”