betaHMM

betaHMM utilizes a homogeneous hidden Markov model to effectively model untransformed beta values and identify differentially methylated CpGs (DMCs) while considering the spatial correlation of adjacent CpG sites.

Key Features:

• Hidden Markov Model Framework: Employs a homogeneous hidden Markov model for statistical inference on DNA methylation data.
• Beta Value Modeling: Effectively models untransformed beta values without requiring data transformation.
• Spatial Correlation Integration: Accounts for spatial correlation of adjacent CpG sites in differential methylation analysis.

Scientific Applications:

• Differentially Methylated CpG Identification: Identifies differentially methylated CpGs (DMCs) from beta-valued DNA methylation data.

Methodology:

A homogeneous hidden Markov model applied to untransformed beta values with spatial correlation of adjacent CpG sites.