Dataset Information

Adjusting for batch effects in DNA methylation microarray data, a lesson learned

ABSTRACT: It is well known, but frequently overlooked, that low- and high-throughput molecular data may contain batch effects, i.e., systematic technical variation. Confounding of experimental batches with the variable(s) of interest is especially concerning, as a batch effect may then be interpreted as a biologically significant finding. An integral step towards reducing false discovery in molecular data analysis includes inspection for batch effects and application of computational tools to reduce this signal if present. In a 30-sample pilot Illumina Infinium HumanMethylation450 (450k array) experiment, we identified two sources of batch effects: array row and chip. Here, we demonstrate two approaches taken to process the 450k data in which an R function, ComBat, was applied to adjust for this non-biological signal. In the “initial analysis”, the application of ComBat to an unbalanced study design resulted in 9,683 and 19,192 significant (FDR<0.05) DNA methylation differences, despite none present prior to correction. Suspicious of this dramatic change, a “revised processing” included changes to our analysis as well as a greater number of samples, and successfully reduced batch effects without introducing false signal. Our work supports conclusions made by an article previously published in this journal: though the ultimate antidote to batch effects is thoughtful study design, every DNA methylation microarray analysis should inspect, assess and, if necessary, adjust for batch effects. The analysis experience presented here can serve as a reminder to the broader community to establish research questions a priori, ensure that they match with study design and encourage communication between technicians and analysts.

ORGANISM(S): Homo sapiens

PROVIDER: GSE108567 | GEO | 2018/07/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Abstract The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina Human Methylation 450k BeadChip is widely used to quantify DNA methylation, nevertheless the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate datasets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished datasets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that a) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; b) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); c) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive dataset suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450k arrays. DNA samples from peripheral blood or CD14+ monocytes were included in the study. DNA methylation levels were profiled using Illumina 450K arrays. Specifically, 50 biological sample replicates from PB and 36 biological sample replicates from monocytes were randomly assigned to 8 BeadChips with technical replicates and processed in one run (a total of 96 DNA samples). Eight samples were technically replicated in pairs, while one sample was represented in a trio of replicates. Different analysis pipelines were compared, however, the file uploaded refers to the best scored. In our publication we used this one to make all analyses and conclusions.

Dataset Information

Adjusting for batch effects in DNA methylation microarray data, a lesson learned

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets