↓ Skip to main content

Identifying and correcting epigenetics measurements for systematic sources of variation

Overview of attention for article published in Clinical Epigenetics, March 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)

Mentioned by

twitter
9 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
48 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Identifying and correcting epigenetics measurements for systematic sources of variation
Published in
Clinical Epigenetics, March 2018
DOI 10.1186/s13148-018-0471-6
Pubmed ID
Authors

Flavie Perrier, Alexei Novoloaca, Srikant Ambatipudi, Laura Baglietto, Akram Ghantous, Vittorio Perduca, Myrto Barrdahl, Sophia Harlid, Ken K. Ong, Alexia Cardona, Silvia Polidoro, Therese Haugdahl Nøst, Kim Overvad, Hanane Omichessan, Martijn Dollé, Christina Bamia, José Marìa Huerta, Paolo Vineis, Zdenko Herceg, Isabelle Romieu, Pietro Ferrari

Abstract

Methylation measures quantified by microarray techniques can be affected by systematic variation due to the technical processing of samples, which may compromise the accuracy of the measurement process and contribute to bias the estimate of the association under investigation. The quantification of the contribution of the systematic source of variation is challenging in datasets characterized by hundreds of thousands of features.In this study, we introduce a method previously developed for the analysis of metabolomics data to evaluate the performance of existing normalizing techniques to correct for unwanted variation. Illumina Infinium HumanMethylation450K was used to acquire methylation levels in over 421,000 CpG sites for 902 study participants of a case-control study on breast cancer nested within the EPIC cohort. The principal component partial R-square (PC-PR2) analysis was used to identify and quantify the variability attributable to potential systematic sources of variation. Three correcting techniques, namely ComBat, surrogate variables analysis (SVA) and a linear regression model to compute residuals were applied. The impact of each correcting method on the association between smoking status and DNA methylation levels was evaluated, and results were compared with findings from a large meta-analysis. A sizeable proportion of systematic variability due to variables expressing 'batch' and 'sample position' within 'chip' was identified, with values of the partial R2statistics equal to 9.5 and 11.4% of total variation, respectively. After application of ComBat or the residuals' methods, the contribution was 1.3 and 0.2%, respectively. The SVA technique resulted in a reduced variability due to 'batch' (1.3%) and 'sample position' (0.6%), and in a diminished variability attributable to 'chip' within a batch (0.9%). After ComBat or the residuals' corrections, a larger number of significant sites (k = 600 andk = 427, respectively) were associated to smoking status than the SVA correction (k = 96). The three correction methods removed systematic variation in DNA methylation data, as assessed by the PC-PR2, which lent itself as a useful tool to explore variability in large dimension data. SVA produced more conservative findings than ComBat in the association between smoking and DNA methylation.

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

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 27%
Student > Ph. D. Student 8 17%
Other 4 8%
Student > Master 4 8%
Student > Bachelor 4 8%
Other 9 19%
Unknown 6 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 29%
Agricultural and Biological Sciences 10 21%
Medicine and Dentistry 4 8%
Computer Science 3 6%
Environmental Science 2 4%
Other 6 13%
Unknown 9 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 04 March 2019.
All research outputs
#2,667,277
of 14,437,320 outputs
Outputs from Clinical Epigenetics
#180
of 758 outputs
Outputs of similar age
#74,400
of 276,734 outputs
Outputs of similar age from Clinical Epigenetics
#1
of 1 outputs
Altmetric has tracked 14,437,320 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 758 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has done well, scoring higher than 75% 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 276,734 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 73% 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