Navigating Illumina Methylation Data: Biology vs. Technical Artefacts

Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains a challenge within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score), and consistently methylated CpGs (CM-Score), that rely on biological features rather than internal array controls. These scores were explored and benchmarked across independent study cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost the reproducibility of the Illumina-like normalization algorithm. In conclusion, our study delivers valuable insights, practical recommendations, and R functions designed to enhance the reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types. Six paired samples from patients with Burkitt lymphoma, once formalin-fixed paraffin-embedded (FFPE) tissue and once cryo preserved material, as well as 5 non-neoplastic samples were collected from the ICGC MMML-Seq consortium. In addition, 1 sample from the peripheral blood of healthy donors were used as normalization sample.