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Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis

Overview of attention for article published in BMC Bioinformatics, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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13 Dimensions

Readers on

mendeley
58 Mendeley
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1 CiteULike
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Title
Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1498-y
Pubmed ID
Authors

Seung Hoan Choi, Adam T. Labadorf, Richard H. Myers, Kathryn L. Lunetta, Josée Dupuis, Anita L. DeStefano

Abstract

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

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

Geographical breakdown

Country Count As %
Czechia 1 2%
Italy 1 2%
Unknown 56 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Researcher 15 26%
Student > Master 11 19%
Student > Postgraduate 5 9%
Student > Bachelor 3 5%
Other 5 9%
Unknown 4 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 29%
Agricultural and Biological Sciences 16 28%
Computer Science 5 9%
Medicine and Dentistry 5 9%
Physics and Astronomy 2 3%
Other 5 9%
Unknown 8 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 09 February 2017.
All research outputs
#1,845,536
of 12,378,687 outputs
Outputs from BMC Bioinformatics
#810
of 4,543 outputs
Outputs of similar age
#68,661
of 335,489 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 132 outputs
Altmetric has tracked 12,378,687 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,543 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,489 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.