An evaluation of processing methods for HumanMethylation450 BeadChip data

BMC Genomics

Methodology Article

Jie Liu¹  Kimberly D. Siegmund² 

[1] Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, Los Angeles, USA;Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, Los Angeles, USA;Department of Preventive Medicine, USC Keck School of Medicine, 2001 N. Soto Street, Suite 202 W, 90089, Los Angeles, CA, USA;

关键词: HumanMethylation450 BeadChip;    Preprocessing;    Normalization;    Batch correction;    Concordance plot;   

DOI  :  10.1186/s12864-016-2819-7

 received in 2015-10-09, accepted in 2016-06-08,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2016