Batch effect correction for genome-wide methylation data with Illumina Infinium platformReportar como inadecuado

Batch effect correction for genome-wide methylation data with Illumina Infinium platform - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BMC Medical Genomics

, 4:84

Bioinformatic and algorithmical studies


BackgroundGenome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data.

MethodsWe evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average β value QNβ; two step quantile normalization at probe signals implemented in -lumi- package of R lumi; and quantile normalization of A and B signal separately ABnorm. Subsequent Empirical Bayes EB batch adjustment was also evaluated.

ResultsEach normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects Dataset 1, normalization alone appeared adequate and -lumi- showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets.

ConclusionGenome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.

Electronic supplementary materialThe online version of this article doi:10.1186-1755-8794-4-84 contains supplementary material, which is available to authorized users.

Download fulltext PDF

Autor: Zhifu Sun - High Seng Chai - Yanhong Wu - Wendy M White - Krishna V Donkena - Christopher J Klein - Vesna D Garovic -



Documentos relacionados