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An evaluation of processing methods for HumanMethylation450 BeadChip data

Overview of attention for article published in BMC Genomics, June 2016
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
An evaluation of processing methods for HumanMethylation450 BeadChip data
Published in
BMC Genomics, June 2016
DOI 10.1186/s12864-016-2819-7
Pubmed ID
Authors

Jie Liu, Kimberly D. Siegmund

Abstract

Illumina's HumanMethylation450 arrays provide the most cost-effective means of high-throughput DNA methylation analysis. As with other types of microarray platforms, technical artifacts are a concern, including background fluorescence, dye-bias from the use of two color channels, bias caused by type I/II probe design, and batch effects. Several approaches and pipelines have been developed, either targeting a single issue or designed to address multiple biases through a combination of methods. We evaluate the effect of combining separate approaches to improve signal processing. In this study nine processing methods, including both within- and between- array methods, are applied and compared in four datasets. For technical replicates, we found both within- and between-array methods did a comparable job in reducing variance across replicates. For evaluating biological differences, within-array processing always improved differential DNA methylation signal detection over no processing, and always benefitted from performing background correction first. Combinations of within-array procedures were always among the best performing methods, with a slight advantage appearing for the between-array method Funnorm when batch effects explained more variation in the data than the methylation alterations between cases and controls. However, when this occurred, RUVm, a new batch correction method noticeably improved reproducibility of differential methylation results over any of the signal-processing methods alone. The comparisons in our study provide valuable insights in preprocessing HumanMethylation450 BeadChip data. We found the within-array combination of Noob + BMIQ always improved signal sensitivity, and when combined with the RUVm batch-correction method, outperformed all other approaches in performing differential DNA methylation analysis. The effect of the data processing method, in any given data set, was a function of both the signal and noise.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 <1%
Unknown 101 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 25%
Researcher 20 20%
Student > Master 11 11%
Student > Bachelor 8 8%
Student > Doctoral Student 7 7%
Other 14 14%
Unknown 17 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 25%
Agricultural and Biological Sciences 25 25%
Medicine and Dentistry 8 8%
Computer Science 6 6%
Neuroscience 4 4%
Other 13 13%
Unknown 21 21%
Attention Score in Context

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 02 September 2017.
All research outputs
#6,815,321
of 22,879,161 outputs
Outputs from BMC Genomics
#3,088
of 10,666 outputs
Outputs of similar age
#111,649
of 352,770 outputs
Outputs of similar age from BMC Genomics
#57
of 174 outputs
Altmetric has tracked 22,879,161 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 10,666 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 70% 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 352,770 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 67% of its contemporaries.
We're also able to compare this research output to 174 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.