scMET

scMET employs a hierarchical Bayesian model to overcome sparsity in single-cell DNA methylome data by sharing information across cells and genomic features, enabling robust quantification of genuine biological methylation heterogeneity.

Key Features:

• Heterogeneity quantification: scMET shares information across cells and genomic features to robustly quantify genuine biological methylation heterogeneity despite sparse coverage.
• Highly variable feature identification: scMET identifies highly variable features that drive epigenetic heterogeneity.
• Differential methylation analysis: scMET performs differential methylation analysis between cells or conditions.
• Differential variability analysis: scMET performs differential variability analysis to detect differences in methylation heterogeneity.

Scientific Applications:

• Characterization of epigenetically distinct cell populations: scMET facilitates the identification and characterization of cell populations with distinct epigenetic profiles.
• Epigenetic regulation of gene expression: scMET enables the formulation of novel hypotheses on how DNA methylation heterogeneity regulates gene expression.

Methodology:

scMET uses a hierarchical Bayesian model that shares information across cells and genomic features to overcome sparsity in high-throughput single-cell DNA methylome measurements.