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A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

Overview of attention for article published in BMC Bioinformatics, July 2009
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1 tweeter

Citations

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131 Dimensions

Readers on

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107 Mendeley
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9 CiteULike
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1 Connotea
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Title
A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests
Published in
BMC Bioinformatics, July 2009
DOI 10.1186/1471-2105-10-209
Pubmed ID
Authors

Antonio Carvajal-Rodríguez, Jacobo de Uña-Alvarez, Emilio Rolán-Alvarez

Abstract

The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.

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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 5%
United Kingdom 2 2%
Norway 1 <1%
Sweden 1 <1%
Switzerland 1 <1%
South Africa 1 <1%
Spain 1 <1%
Denmark 1 <1%
Unknown 94 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 28%
Researcher 30 28%
Professor 11 10%
Student > Master 10 9%
Professor > Associate Professor 6 6%
Other 14 13%
Unknown 6 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 59%
Biochemistry, Genetics and Molecular Biology 9 8%
Medicine and Dentistry 6 6%
Mathematics 6 6%
Computer Science 3 3%
Other 14 13%
Unknown 6 6%

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 21 June 2012.
All research outputs
#9,906,118
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,814
of 4,576 outputs
Outputs of similar age
#85,667
of 119,934 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 59 outputs
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