methylation-variability-analysis

You can quantify CpG methylation variability by building a multi-sample matrix from WGBS data and computing statistics like variance, MAD, and CV. This identifies highly variable CpGs to reveal epigenetic heterogeneity across biological samples.

Epigenetic heterogeneity analysis identifies highly variable CpGs (HVCs) across multiple samples to reveal biological diversity. You need it when comparing WGBS methylation differences between groups or conditions to understand variability beyond average methylation levels.

Yes, analyzing methylation variability requires R and methylKit-like workflows. You must also provide your own genome assembly and standard methylation BED columns to properly construct the multi-sample CpG matrix and compute the variability statistics.

Yes, you can detect differential variability between conditions using WGBS BED files. The workflow computes between-sample variability metrics and extends to region-level analyses, allowing you to contrast differential variability across different biological groups.

The best way to identify highly variable CpGs is by computing variance, MAD, and CV metrics across a multi-sample WGBS matrix. This approach detects top-variable CpGs and generates visualizations to compare epigenetic heterogeneity between groups.

Your CpG methylation variability analysis requires a user-provided genome assembly because the workflow needs exact genomic coordinates to properly construct the multi-sample matrix. This ensures accurate alignment of methylation BED columns across all samples.