scMET

scMET models single-cell DNA methylation using a hierarchical Bayesian framework to quantify methylation levels and biological heterogeneity while addressing sparse CpG coverage.

Key Features:

• Hierarchical Bayesian model: employs a hierarchical beta-binomial specification integrated within a generalized linear model to capture biological overdispersion.
• Information sharing across cells and genomic features: leverages pooling of information across cells and genomic features to mitigate sparse CpG coverage.
• Quantification of variability: provides biologically interpretable estimates of methylation variability and identifies highly variable features.
• Differential analysis: performs differential methylation and differential variability analyses between pre-specified groups of cells.
• Feature selection: implements feature selection to characterize epigenetically distinct cell populations and identify key regulatory elements.

Scientific Applications:

• Characterization of epigenetic heterogeneity: study DNA methylation heterogeneity at single-cell resolution to inform gene regulation.
• Formulation of biological hypotheses: estimate methylation variability to generate hypotheses about epigenetic regulation, including during early developmental stages.
• Analysis of large-scale datasets: applicable to recent large-scale single-cell methylation datasets for comparative and population-level analyses.

Methodology:

Uses a hierarchical Bayesian framework with a beta-binomial model embedded in a generalized linear model, incorporates information sharing across cells and genomic features, and supports inference on overdispersion as well as differential methylation and differential variability.