methylation-region-identification

methylation-region-identification

Summary

Identify differentially methylated regions (DMRs) in DNA methylation array data (EPIC or 450k) using ChAMP's integration of bumphunter-based detection. This skill applies statistical region-level analysis to detect contiguous genomic regions with coordinated methylation changes between sample groups.

When to use

You have loaded normalized methylation beta-value matrices from Illumina EPIC or 450k arrays and need to move beyond single-CpG differential methylation testing to identify multi-CpG regions with coordinated differential methylation signals. Use this skill when your analysis goal requires region-level rather than probe-level inference, or when you want to aggregate statistical evidence across neighboring CpGs to improve power and biological interpretability.

When NOT to use

• Your input is already a pre-computed feature matrix or region-level methylation summary (DMRs would be double-counted).
• You have fewer than ~3–4 samples per group, as bumphunter-based region detection relies on adequate statistical power from replicate samples.
• Your analysis requires fixed-width genomic windows rather than data-driven region boundaries (use fixed-window tiling instead).

Inputs

• Normalized DNA methylation beta-value matrix (EPIC or 450k array)
• Sample group/phenotype labels
• ChAMP-compatible methylation object (from champ.load() or external normalization)

Outputs

• List of detected differentially methylated regions (DMRs) with genomic coordinates
• DMR summary table (position, size, CpG count, statistical significance)
• DMR count summary

How to apply

Load your normalized EPIC or 450k methylation dataset into ChAMP (either from .idat files or a pre-normalized beta-value matrix). Call champ.DMR() with bumphunter-based detection to scan the genome for contiguous regions of differential methylation across your sample groups. The function applies bumphunter's algorithm to identify candidate regions by smoothing and thresholding methylation differences, then filters regions based on minimum CpG count (regions with only 1–2 CpGs are excluded by default). Extract and count the detected DMRs from the function output; typical results on simulated EPIC data yield approximately 4700+ DMRs depending on the simulation parameters and CpG density filtering thresholds.