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Response to: Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies

Overview of attention for article published in Genome Biology (Online Edition), January 2017
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1 tweeter

Citations

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4 Dimensions

Readers on

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7 Mendeley
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Title
Response to: Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies
Published in
Genome Biology (Online Edition), January 2017
DOI 10.1186/s13059-017-1149-7
Pubmed ID
Authors

Kevin McGregor, Aurélie Labbe, Celia M. T. Greenwood

Abstract

We thank Hattab and colleagues for their correspondence and their investigation of cell-type mixture correction methods in methyl-CG binding domain sequencing. Here, we speculate on why surrogate variable analysis (SVA) performed differently between their two data sets, and poorly in one of them.Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1148-8 and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y.

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

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 29%
Student > Ph. D. Student 2 29%
Student > Master 1 14%
Student > Bachelor 1 14%
Professor > Associate Professor 1 14%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 57%
Nursing and Health Professions 1 14%
Mathematics 1 14%
Psychology 1 14%

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 31 January 2017.
All research outputs
#7,789,817
of 8,987,048 outputs
Outputs from Genome Biology (Online Edition)
#2,411
of 2,471 outputs
Outputs of similar age
#255,366
of 309,180 outputs
Outputs of similar age from Genome Biology (Online Edition)
#60
of 65 outputs
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So far Altmetric has tracked 2,471 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.