mist

mist models DNA methylation trajectories and performs differential methylation analysis in single-cell DNA methylation data through a hierarchical Bayesian framework.

Key Features:

• Trajectory Modeling: mist estimates developmental-stage-specific variations in DNA methylation along pseudotime.
• Feature Identification: mist identifies genomic features with drastic methylation changes along pseudotime.
• Differential Methylation Detection: For two phenotypic groups, mist detects features with distinct temporal methylation patterns.
• Parameter Estimation: mist uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations.

Scientific Applications:

• Single-cell DNA Methylation Analysis: mist enables differential methylation analysis in single-cell DNA methylation (scDNAm) data.
• Developmental Trajectory Analysis: mist supports analysis of methylation dynamics across developmental stages and pseudotime.
• Comparative Phenotypic Studies: mist identifies differentially methylated features between two phenotypic groups with distinct temporal patterns.

Methodology:

mist employs a hierarchical Bayesian framework and uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations.