Identifying Mislabeled And Contaminated Dna Methylation Microarray Data: An Extended Quality Control Toolset With Examples From Geo

Jonathan A. Heiss & Allan C. Just
Background: Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets. Methods: Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discordant samples, and both an identity check for fingerprinting...
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