title: An evaluation of processing methods for HumanMethylation450 BeadChip data.
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.
authors.affiliation: Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, Los Angeles, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, Los Angeles, USA. kims@usc.edu.|Department of Preventive Medicine, USC Keck School of Medicine, 2001 N. Soto Street, Suite 202 W, Los Angeles, CA, 90089, USA. kims@usc.edu.
authors.forename: Kimberly D
authors.initials: KD
authors.lastname: Siegmund
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics; D001921:Brain / Q000378:metabolism; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods*; D005260:Female; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results
keywords: Batch correction; Concordance plot; HumanMethylation450 BeadChip; Normalization; Preprocessing