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Robust joint score tests in the application of DNA methylation data analysis

Overview of attention for article published in BMC Bioinformatics, May 2018
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Title
Robust joint score tests in the application of DNA methylation data analysis
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2185-3
Pubmed ID
Authors

Xuan Li, Yuejiao Fu, Xiaogang Wang, Weiliang Qiu

Abstract

Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW's method seems to be quite conservative and has not been fully compared with existing joint tests. We proposed three improved joint score tests, namely iAW.Lev, iAW.BF, and iAW.TM, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls; 2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina HumanMethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium MethylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW. The three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, iAW.BF seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 42%
Student > Master 2 17%
Student > Ph. D. Student 2 17%
Unspecified 1 8%
Other 1 8%
Other 1 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 33%
Engineering 2 17%
Agricultural and Biological Sciences 2 17%
Mathematics 1 8%
Unspecified 1 8%
Other 2 17%

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 28 May 2018.
All research outputs
#10,368,113
of 12,996,278 outputs
Outputs from BMC Bioinformatics
#4,030
of 4,847 outputs
Outputs of similar age
#203,147
of 271,013 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 22 outputs
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We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.