MethylSeqDesign

MethylSeqDesign provides power calculation and study design for bisulfite DNA methylation sequencing (Methyl-Seq) experiments.

Key Features:

• Power Calculation Framework: Uses pilot Methyl-Seq data to estimate study power and inform sample-size and design decisions.
• Beta-Binomial Model for Differential Methylation Analysis: Models per-site or per-region methylation counts with a beta-binomial model to account for over-dispersion.
• Mixture Model Fitting and Parametric Bootstrap Procedure: Fits mixture models to p-value distributions derived from pilot data and applies a parametric bootstrap to generate power estimates.
• Targeted Region Inference: Focuses inference on pre-specified targeted regions to limit analysis across tens of millions of methylation sites.
• Simulation-Based Performance Evaluation: Evaluates method performance and robustness using simulation studies.
• Real-World Application Examples: Validated on two real Methyl-Seq datasets to demonstrate practical applicability.

Scientific Applications:

• Design for Differential Methylation Detection: Design Methyl-Seq experiments with sufficient statistical power to detect differential methylation across groups.
• Cancer and Disease Epigenetics: Apply to cancer research and other disease-related epigenetic studies to identify methylation changes.
• Developmental and Environmental Epigenomics: Apply to developmental biology and environmental studies affecting gene expression to assess methylation patterns.

Methodology:

Leverages pilot Methyl-Seq data, fits beta-binomial models for differential methylation, fits mixture models to p-values, performs a parametric bootstrap for power estimation, evaluates via simulation studies, and restricts inference to pre-specified targeted regions.