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Related Experiment Video

Updated: Feb 9, 2026

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
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Identifying mislabeled and contaminated DNA methylation microarray data: an extended quality control toolset with

Jonathan A Heiss1, Allan C Just1

  • 1Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029 NY USA.

Clinical Epigenetics
|June 9, 2018
PubMed
Summary

Implementing robust quality control checks for DNA methylation microarray data is crucial for reliable epigenome-wide association studies. This study introduces a comprehensive quality control workflow to identify mislabeled or contaminated samples, enhancing data integrity and reproducibility.

Keywords:
450KContaminationDNA methylationData cleaningEPICEpigenomicsInfiniumMislabelingQuality control

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Area of Science:

  • Epigenetics
  • Genomics
  • Bioinformatics

Background:

  • Mislabeled, contaminated, or poorly performing samples can compromise the accuracy and power of methylation microarray analyses.
  • Such issues can lead to spurious associations, impacting the reliability of epigenome-wide association studies (EWAS).

Purpose of the Study:

  • To describe and apply a set of quality checks for Illumina 450K and EPIC microarrays.
  • To identify problematic samples in publicly available DNA methylation datasets.
  • To improve the quality and reproducibility of EWAS findings.

Main Methods:

  • Utilized 17 manufacturer-defined control metrics, a sex check for mislabeled samples, and an identity check for donor fingerprinting.
  • Developed a contamination measure based on high-frequency single nucleotide polymorphism (SNP) probes.
  • Tested these checks on 80 datasets (8327 samples) from the GEO repository using the 450K microarray.

Main Results:

  • 940 samples were flagged by control metrics; 133 samples (from 20 datasets) had incorrect sex assignments.
  • The SNP probe-based contamination measure showed high correlation (>0.95) with an independent contamination assessment in a contaminated dataset.

Conclusions:

  • Thorough quality control is essential for identifying mislabeled, contaminated, or technically problematic samples.
  • Quality control issues are prevalent in public DNA methylation data repositories.
  • Advocates for enhanced quality control workflows in EWAS and provides a software package for implementation.