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Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective

Overview of attention for article published in BMC Bioinformatics, March 2015
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  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

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9 tweeters

Citations

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62 Dimensions

Readers on

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83 Mendeley
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2 CiteULike
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Title
Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/s12859-015-0527-y
Pubmed ID
Authors

E Andres Houseman, Karl T Kelsey, John K Wiencke, Carmen J Marsit

Abstract

The impact of cell-composition effects in analysis of DNA methylation data is now widely appreciated. With the availability of a reference data set consisting of DNA methylation measurements on isolated cell types, it is possible to impute cell proportions and adjust for them, but there is increasing interest in methods that adjust for cell composition effects when reference sets are incomplete or unavailable. In this article we present a theoretical basis for one such method, showing that the total effect of a phenotype on DNA methylation can be decomposed into orthogonal components, one representing the effect of phenotype on proportions of major cell types, the other representing either subtle effects in composition or global effects at focused loci, and that it is possible to separate these two types of effects in a finite data set. We demonstrate this principle empirically on nine DNA methylation data sets, showing that the first few principal components generally contain a majority of the information on cell-type present in the data, but that later principal components nevertheless contain information about a small number of loci that may represent more focused associations. We also present a new method for determining the number of linear terms to interpret as cell-mixture effects and demonstrate robustness to the choice of this parameter. Taken together, our work demonstrates that reference-free algorithms for cell-mixture adjustment can produce biologically valid results, separating cell-mediated epigenetic effects (i.e. apparent effects arising from differences in cell composition) from those that are not cell mediated, and that in general the interpretation of associations evident from DNA methylation should be carefully considered.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Denmark 1 1%
Canada 1 1%
Brazil 1 1%
Unknown 79 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 17 20%
Student > Doctoral Student 12 14%
Student > Master 9 11%
Student > Bachelor 8 10%
Other 11 13%
Unknown 7 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 37%
Biochemistry, Genetics and Molecular Biology 14 17%
Medicine and Dentistry 9 11%
Computer Science 6 7%
Mathematics 3 4%
Other 9 11%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 August 2015.
All research outputs
#3,433,751
of 12,910,847 outputs
Outputs from BMC Bioinformatics
#1,532
of 4,814 outputs
Outputs of similar age
#59,775
of 220,703 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 12,910,847 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 4,814 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 67% 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 220,703 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them