• Publications
  • Influence
Statistical significance for genomewide studies
With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of featuresExpand
  • 7,892
  • 957
The positive false discovery rate: a Bayesian interpretation and the q-value
Multiple hypothesis testing is concerned with controlling the rate of false positives when testing several hypotheses simultaneously. One multiple hypothesis testing error measure is the falseExpand
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  • 217
Empirical Bayes Analysis of a Microarray Experiment
Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raisingExpand
  • 1,642
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Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) ofExpand
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Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae
We have analyzed the translational status of each mRNA in rapidly growing Saccharomyces cerevisiae. mRNAs were separated by velocity sedimentation on a sucrose gradient, and 14 fractions across theExpand
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Precision and functional specificity in mRNA decay
Posttranscriptional processing of mRNA is an integral component of the gene expression program. By using DNA microarrays, we precisely measured the decay of each yeast mRNA, after thermalExpand
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Significance analysis of time course microarray experiments.
Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifyingExpand
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Mapping the Genetic Architecture of Gene Expression in Human Liver
Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-orderExpand
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A general framework for multiple testing dependence
  • J. Leek, J. Storey
  • Medicine, Computer Science
  • Proceedings of the National Academy of Sciences
  • 2 December 2008
We develop a general framework for performing large-scale significance testing in the presence of arbitrarily strong dependence. We derive a low-dimensional set of random vectors, called a dependenceExpand
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The optimal discovery procedure: a new approach to simultaneous significance testing
The Neyman-Pearson lemma provides a simple procedure for optimally testing a single hypothesis when the null and alternative distributions are known. This result has played a major role in theExpand
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