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Comparison of pre-processing methodologies for Illumina 450k methylation array data in familial analyses

Overview of attention for article published in Clinical Epigenetics, July 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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10 tweeters

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6 Dimensions

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Title
Comparison of pre-processing methodologies for Illumina 450k methylation array data in familial analyses
Published in
Clinical Epigenetics, July 2016
DOI 10.1186/s13148-016-0241-2
Pubmed ID
Authors

Emma Cazaly, Russell Thomson, James R. Marthick, Adele F. Holloway, Jac Charlesworth, Joanne L. Dickinson

Abstract

Human methylome mapping in health and disease states has largely relied on Illumina Human Methylation 450k array (450k array) technology. Accompanying this has been the necessary evolution of analysis pipelines to facilitate data processing. The majority of these pipelines, however, cater for experimental designs where matched 'controls' or 'normal' samples are available. Experimental designs where no appropriate 'reference' exists remain challenging. Herein, we use data generated from our study of the inheritance of methylome profiles in families to evaluate the performance of eight normalisation pre-processing methods. Fifty individual samples representing four families were interrogated on five 450k array BeadChips. Eight normalisation methods were tested using qualitative and quantitative metrics, to assess efficacy and suitability. Stratified quantile normalisation combined with ComBat were consistently found to be the most appropriate when assessed using density, MDS and cluster plots. This was supported quantitatively by ANOVA on the first principal component where the effect of batch dropped from p < 0.01 to p = 0.97 after stratified QN and ComBat. Median absolute differences between replicated samples were the lowest after stratified QN and ComBat as were the standard error measures on known imprinted regions. Biological information was preserved after normalisation as indicated by the maintenance of a significant association between a known mQTL and methylation (p = 1.05e-05). A strategy combining stratified QN with ComBat is appropriate for use in the analyses when no reference sample is available but preservation of biological variation is paramount. There is great potential for use of 450k array data to further our understanding of the methylome in a variety of similar settings. Such advances will be reliant on the determination of appropriate methodologies for processing these data such as established here.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Uruguay 1 2%
Belgium 1 2%
Unknown 45 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Student > Bachelor 10 21%
Student > Ph. D. Student 8 17%
Student > Master 4 9%
Student > Doctoral Student 2 4%
Other 8 17%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 30%
Biochemistry, Genetics and Molecular Biology 11 23%
Engineering 5 11%
Computer Science 4 9%
Psychology 1 2%
Other 7 15%
Unknown 5 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 2017.
All research outputs
#3,519,867
of 15,043,331 outputs
Outputs from Clinical Epigenetics
#212
of 780 outputs
Outputs of similar age
#64,652
of 260,878 outputs
Outputs of similar age from Clinical Epigenetics
#2
of 8 outputs
Altmetric has tracked 15,043,331 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 780 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 gotten more attention than average, scoring higher than 72% 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 260,878 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 75% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.