On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles

By Christina Kendziorski, Michael A. Newton, H. Lan, Michael N. Gould. Statistics in Medicine, 2003

396 citations recorded by the source corpus.

Subfields: Genetics, Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4d65e9ceb2981fc081f42544b5110ccd07d28976

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Bradley Efron
2. Gordon K. Smyth
3. Terence P. Speed
4. David B. Dahl
5. Michael A. Newton
6. Hamish S. Scott
7. Joëlle Michaud
8. M. Kloareg

In Management Science and Operations Research

1. Bradley Efron
2. Terence P. Speed
3. David B. Dahl
4. Michael A. Newton
5. M. Kloareg
6. David Causeur
7. Hamish S. Scott
8. Joëlle Michaud

In Molecular Biology

1. Gordon K. Smyth
2. Bradley Efron
3. Hamish S. Scott
4. Joëlle Michaud
5. Jeffrey T. Leek
6. Terence P. Speed
7. David B. Dahl
8. Michael A. Newton

Citations in the corpus

• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, positive): “Newton et al (2001), Newton and Kendziorski (2003) and Kendziorski et al (2003) have considered empirical Bayes models for expression based on gamma and log-normal distributions.”
• Multiple Hypothesis Testing by Clustering Treatment Effects (2007, methodology): “More formally, using all eight chains, we applied the Gelman and Rubin and (d) None ( BEMMA; LIMMA; EBarrays).”
• A multivariate empirical Bayes statistic for replicated microarray time course data (2006, methodology): “The most widely used method for identifying temporally changing genes in replicated microarray experiments is to carry out multiple pairwise comparisons across times, using statistics developed for comparing two independent samples, [2, 6, 13, 19, 20, 23, 24, 33].”
• Transactivation of miR-34a by p53 broadly influences gene expression and promotes apoptosis. (2007, methodology): “After the signal intensities were averaged across technical replicates, an empirical Bayes method implemented in the bioconductor package EBarrays, was used to estimate the posterior probabilities of the differential expression of genes between the tested conditions (Kendziorski et al., 2003;Newton ”
• Size, power and false discovery rates (2007, background): “Large scale simultaneous testing, however, raises the possibility of detecting deficien-cies in the theoretical null, as with the HIV data in Figure 1 where the z -value histogram is noticeably too narrow around its central peak.”
• Detecting differential gene expression with a semiparametric hierarchical mixture method. (2004, methodology): “The fully parametric case (1) is similar to the Gamma–Gamma (GG) methodology described in Kendziorski et al. (2003), except that here we allow ordered alternative hypotheses.”
• Use of within-array replicate spots for assessing differential expression in microarray experiments (2005, background): “…each individual gene can be made more reliable by making use of information generated from the whole ensemble of genes (Newton et al., 2001; Tusher et al., 2001; Efron et al., 2001; Efron and Tibshirani, 2002; L¨onnstedt and Speed, 2002; Kendziorski et al., 2003; Newton et al., 2004; Smyth, 2004).”
• A Factor Model Approach to Multiple Testing Under Dependence (2009, background): “This has for example led to the empirical Bayes approach by Kendziorski et al. (2003), which takes advantage of the shared information between variables.”