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Empirical Bayes estimation of posterior probabilities of enrichment: A comparative study of five estimators of the local false discovery rate

Overview of attention for article published in BMC Bioinformatics, January 2013
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Title
Empirical Bayes estimation of posterior probabilities of enrichment: A comparative study of five estimators of the local false discovery rate
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-87
Pubmed ID
Authors

Zhenyu Yang, Zuojing Li, David R Bickel

Abstract

In investigating differentially expressed genes or other selected features, researchers conduct hypothesis tests to determine which biological categories, such as those of the Gene Ontology (GO), are enriched for the selected features. Multiple comparison procedures (MCPs) are commonly used to prevent excessive false positive rates. Traditional MCPs, e.g., the Bonferroni method, go to the opposite extreme: strictly controlling a family-wise error rate, resulting in excessive false negative rates. Researchers generally prefer the more balanced approach of instead controlling the false discovery rate (FDR). However, the q-values that methods of FDR control assign to biological categories tend to be too low to reliably estimate the probability that a biological category is not enriched for the preselected features. Thus, we study an application of the other estimators of that probability, which is called the local FDR (LFDR).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
Canada 1 4%
Unknown 23 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 20%
Professor > Associate Professor 5 20%
Other 4 16%
Professor 3 12%
Student > Ph. D. Student 3 12%
Other 5 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 24%
Mathematics 5 20%
Computer Science 5 20%
Unspecified 4 16%
Biochemistry, Genetics and Molecular Biology 2 8%
Other 3 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 March 2013.
All research outputs
#2,310,367
of 4,507,144 outputs
Outputs from BMC Bioinformatics
#1,806
of 2,646 outputs
Outputs of similar age
#44,209
of 89,203 outputs
Outputs of similar age from BMC Bioinformatics
#96
of 131 outputs
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