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Measuring cell-type specific differential methylation in human brain tissue

Overview of attention for article published in Genome Biology, August 2013
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Title
Measuring cell-type specific differential methylation in human brain tissue
Published in
Genome Biology, August 2013
DOI 10.1186/gb-2013-14-8-r94
Pubmed ID
Authors

Carolina M Montaño, Rafael A Irizarry, Walter E Kaufmann, Konrad Talbot, Raquel E Gur, Andrew P Feinberg, Margaret A Taub

Abstract

The behavior of epigenetic mechanisms in the brain is obscured by tissue heterogeneity and disease-related histological changes. Not accounting for these confounders leads to biased results. We develop a statistical methodology that estimates and adjusts for celltype composition by decomposing neuronal and non-neuronal differential signal. This method provides a conceptual framework for deconvolving heterogeneous epigenetic data from postmortem brain studies. We apply it to find cell-specific differentially methylated regions between prefrontal cortex and hippocampus. We demonstrate the utility of the method on both Infinium 450k and CHARM data.

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X Demographics

The data shown below were collected from the profiles of 4 X users 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 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 5%
United Kingdom 2 2%
Ireland 1 <1%
Germany 1 <1%
China 1 <1%
New Zealand 1 <1%
Spain 1 <1%
Russia 1 <1%
Unknown 117 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 30%
Researcher 28 21%
Student > Master 13 10%
Student > Bachelor 11 8%
Other 9 7%
Other 24 18%
Unknown 7 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 40%
Biochemistry, Genetics and Molecular Biology 25 19%
Medicine and Dentistry 16 12%
Neuroscience 11 8%
Computer Science 6 5%
Other 11 8%
Unknown 10 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 24 February 2019.
All research outputs
#15,739,529
of 25,373,627 outputs
Outputs from Genome Biology
#3,967
of 4,467 outputs
Outputs of similar age
#120,160
of 211,836 outputs
Outputs of similar age from Genome Biology
#40
of 49 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 10th percentile – i.e., 10% of its peers scored the same or lower than it.
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 211,836 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.