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Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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13 tweeters
wikipedia
1 Wikipedia page

Citations

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102 Dimensions

Readers on

mendeley
107 Mendeley
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2 CiteULike
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Title
Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL)
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0943-7
Pubmed ID
Authors

Devin C. Koestler, Meaghan J. Jones, Joseph Usset, Brock C. Christensen, Rondi A. Butler, Michael S. Kobor, John K. Wiencke, Karl T. Kelsey

Abstract

Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogenous biospecimens offer a promising solution, however the performance of such methods depends entirely on the library of methylation markers being used for deconvolution. Here, we introduce a novel algorithm for Identifying Optimal Libraries (IDOL) that dynamically scans a candidate set of cell-specific methylation markers to find libraries that optimize the accuracy of cell fraction estimates obtained from cell mixture deconvolution. Application of IDOL to training set consisting of samples with both whole-blood DNA methylation data (Illumina HumanMethylation450 BeadArray (HM450)) and flow cytometry measurements of cell composition revealed an optimized library comprised of 300 CpG sites. When compared existing libraries, the library identified by IDOL demonstrated significantly better overall discrimination of the entire immune cell landscape (p = 0.038), and resulted in improved discrimination of 14 out of the 15 pairs of leukocyte subtypes. Estimates of cell composition across the samples in the training set using the IDOL library were highly correlated with their respective flow cytometry measurements, with all cell-specific R (2)>0.99 and root mean square errors (RMSEs) ranging from [0.97 % to 1.33 %] across leukocyte subtypes. Independent validation of the optimized IDOL library using two additional HM450 data sets showed similarly strong prediction performance, with all cell-specific R (2)>0.90 and R M S E<4.00 %. In simulation studies, adjustments for cell composition using the IDOL library resulted in uniformly lower false positive rates compared to competing libraries, while also demonstrating an improved capacity to explain epigenome-wide variation in DNA methylation within two large publicly available HM450 data sets. Despite consisting of half as many CpGs compared to existing libraries for whole blood mixture deconvolution, the optimized IDOL library identified herein resulted in outstanding prediction performance across all considered data sets and demonstrated potential to improve the operating characteristics of EWAS involving adjustments for cell distribution. In addition to providing the EWAS community with an optimized library for whole blood mixture deconvolution, our work establishes a systematic and generalizable framework for the assembly of libraries that improve the accuracy of cell mixture deconvolution.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
New Zealand 1 <1%
Denmark 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 102 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 26%
Researcher 22 21%
Student > Bachelor 9 8%
Student > Doctoral Student 8 7%
Student > Master 8 7%
Other 12 11%
Unknown 20 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 30 28%
Agricultural and Biological Sciences 21 20%
Medicine and Dentistry 8 7%
Computer Science 7 7%
Immunology and Microbiology 2 2%
Other 12 11%
Unknown 27 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 13 March 2021.
All research outputs
#3,161,837
of 20,477,298 outputs
Outputs from BMC Bioinformatics
#1,204
of 6,782 outputs
Outputs of similar age
#51,798
of 278,274 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 7 outputs
Altmetric has tracked 20,477,298 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,782 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 278,274 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 81% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.