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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "scMET" in publications use:'
type: software
license: GPL-3.0-only
title: 'scMET: Bayesian modelling of cell-to-cell DNA methylation heterogeneity'
version: 1.7.0
abstract: High-throughput single-cell measurements of DNA methylomes can quantify
  methylation heterogeneity and uncover its role in gene regulation. However, technical
  limitations and sparse coverage can preclude this task. scMET is a hierarchical
  Bayesian model which overcomes sparsity, sharing information across cells and genomic
  features to robustly quantify genuine biological heterogeneity. scMET can identify
  highly variable features that drive epigenetic heterogeneity, and perform differential
  methylation and variability analyses. We illustrate how scMET facilitates the characterization
  of epigenetically distinct cell populations and how it enables the formulation of
  novel hypotheses on the epigenetic regulation of gene expression.
authors:
- family-names: Kapourani
  given-names: Andreas C.
  email: kapouranis.andreas@gmail.com
  orcid: https://orcid.org/0000-0003-2303-1953
preferred-citation:
  type: article
  title: 'scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell
    resolution'
  authors:
  - family-names: Kapourani
    given-names: Chantriolnt Andreas
  - family-names: Argelaguet
    given-names: Ricard
  - family-names: Sanguinetti
    given-names: Guido
  - family-names: Vallejos
    given-names: Catalina A.
  journal: Genome biology
  year: '2021'
  volume: '22'
  issue: '1'
repository: https://bioc.r-universe.dev
repository-code: https://github.com/andreaskapou/scMET
url: https://github.com/andreaskapou/scMET
contact:
- family-names: Kapourani
  given-names: Andreas C.
  email: kapouranis.andreas@gmail.com
  orcid: https://orcid.org/0000-0003-2303-1953