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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, January 2017
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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, 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.

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

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 25%
Student > Ph. D. Student 2 25%
Student > Bachelor 1 13%
Student > Master 1 13%
Professor > Associate Professor 1 13%
Other 0 0%
Unknown 1 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 50%
Mathematics 1 13%
Nursing and Health Professions 1 13%
Psychology 1 13%
Unknown 1 13%
Attention Score in Context

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
#22,764,772
of 25,382,440 outputs
Outputs from Genome Biology
#4,395
of 4,468 outputs
Outputs of similar age
#365,083
of 424,069 outputs
Outputs of similar age from Genome Biology
#58
of 61 outputs
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