Abstract

BackgroundIllumina’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.ResultsIn 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2819-7) contains supplementary material, which is available to authorized users.

Highlights

  • Illumina’s HumanMethylation450 arrays provide the most cost-effective means of high-throughput DNA methylation analysis

  • Preprocessing methods In total we evaluate and compare nine preprocessing approaches based on the following within-array and between-array methods: (1) background correction and dye-bias equalization (Noob) [14]; (2) beta-mixture quantile normalization (BMIQ) [6]; (3) subset-quantile within-array normalization (SWAN) [15]; (4) background adjustment followed by between-array normalization performed separately by probe design (Dasen) [13]; (5) subset-quantile normalization (SQN) [11, 16]; and (6) functional normalization (Funnorm) [17]

  • It is worth of noting that SQN changes the distribution of type I probes most noticeably, presumably because it uses type II probes as the anchors when normalizing between two probe designs in each sub-category

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Summary

Introduction

Illumina’s HumanMethylation450 arrays provide the most cost-effective means of high-throughput DNA methylation analysis. We evaluate the effect of combining separate approaches to improve signal processing. DNA methylation, featured by the presence of 5methylcytosine in the context of CpG dinucleotides, is the most studied form of epigenetic modification. It plays an important role in both physiological processes and disease states. Alterations in DNA methylation landscapes include a global hypomethylation of the genome accompanied with CpG island hypermethylation [1]. The Illumina Infinium HumanMethylation450 (HM450) BeadChip is a popular technology for large-scale DNA methylation profiling due to its advantage in reagent cost and time, comprehensive coverage and high throughput

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