Statistical approaches for the analysis of DNA methylation microarray data |
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Authors: | Kimberly D Siegmund |
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Institution: | (1) Department of Preventive Medicine, Keck School of Medicine of USC, 1540 Alcazar Street, CHP 220C, Los Angeles, CA 90089, USA |
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Abstract: | Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the
number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications,
biases caused by technical issues are of concern. Some of these issues are old (e.g., two-color dye bias and probe- and array-specific
effects), while others are new (e.g., fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics
of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I
then describe the microarray technologies most commonly in use, along with the methods used for preprocessing and obtaining
a summary measure. I finish with a section describing downstream analyses of the data, focusing on methods that model percentage
DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data. |
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Keywords: | |
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