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Melissa: Bayesian clustering and imputation of single-cell methylomes.

ABSTRACT: Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epigenetic variability between cells. The clustering also acts as an effective regularization for data imputation on unassayed CpG sites, enabling transfer of information between individual cells. We show both on simulated and real data sets that Melissa provides accurate and biologically meaningful clusterings and state-of-the-art imputation performance.

SUBMITTER: Kapourani CA

PROVIDER: S-EPMC6427844 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Melissa: Bayesian clustering and imputation of single-cell methylomes.

Kapourani Chantriolnt-Andreas CA Sanguinetti Guido G

Genome biology 20190321 1

Measurements of single-cell methylation are revolutionizing our understanding of epigenetic control of gene expression, yet the intrinsic data sparsity limits the scope for quantitative analysis of such data. Here, we introduce Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical method to cluster cells based on local methylation patterns, discovering patterns of epigenetic variability between cells. The clustering also acts as an effective regularization for data im ...[more]

PMID: 30898142

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