Adjusting for batch effects in DNA methylation microarray data, a lesson learned
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PROVIDER: PRJNA427719 | ENA |
REPOSITORIES: ENA
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Project description: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.
2018-07-01 | GSE108567 | GEO
Project description: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 toward reducing false discovery in molecular data analysis includes inspection for batch effects and accounting for this signal if present. In a 30-sample pilot Illumina Infinium HumanMethylation450 (450k array) experiment, we identified two sources of batch effects: 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 the non-biological signal. In the "initial analysis," the application of ComBat to an unbalanced study design resulted in 9,612 and 19,214 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, account 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.
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Project description:Microarray is a powerful technique that has been used extensively for genome-wide gene expression analysis. Several different microarray technologies are available, but lack of standardization makes it challenging to compare and integrate data from different platforms. Furthermore, batch related biases within datasets are common, but are often not tackled prior to the data analysis, potentially affecting the end results. In the current study, a set of 234 breast cancer samples were analyzed on two different microarray platforms. The aim was to compare and evaluate the reproducibility and accuracy of gene expression measurements obtained from our in-house 29K array platform with data from Agilent SurePrint G3 microarray platform. The 29K dataset contained known batch-effects associated with the fabrication procedure. We here demonstrate how the use of ComBat batch adjustments method can unmask true biological signals by successfully overcoming systematic technical variations caused by differences between fabrication batches and microarray platforms. Paired correlation analysis revealed a high level of consistency between data obtained from the 29K gene expression platform and Agilent SurePrint G3 platform, which could be further improved by ComBat batch adjustment. Particularly high-variance genes were found to be highly reproducibly expressed across platforms. Furthermore, high concordance rates were observed both for prediction of estrogen receptor status and intrinsic molecular breast cancer subtype classification, two clinical important parameters. In conclusion, the current study emphasizes the importance of utilizing proper batch adjustment methods to reduce systematically technical bias when comparing and integrating data from different fabrication batches and microarray platforms.
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