DESCRIPTION

Package: scMET
Type: Package
Title: Bayesian modelling of cell-to-cell DNA methylation heterogeneity
Version: 0.99.11
Authors@R: 
    c(person(given = "Andreas C.",
           family = "Kapourani",
           role = c("aut", "cre"),
           email = "kapouranis.andreas@gmail.com",
           comment = c(ORCID = "0000-0003-2303-1953")),
      person(given = "John",
           family = "Riddell",
           role = c("ctb")))
Description: 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.
License: GPL-3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.0
Biarch: true
BugReports: https://github.com/andreaskapou/scMET/issues
Depends: 
    R (>= 4.2.0)
Imports: 
    methods,
    Rcpp (>= 1.0.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.21.3),
    rstantools (>= 2.1.0),
    VGAM,
    data.table,
    MASS,
    logitnorm,
    ggplot2,
    matrixStats,
    assertthat,
    viridis,
    coda,
    BiocStyle,
    cowplot,
    stats,
    SummarizedExperiment,
    SingleCellExperiment,
    Matrix,
    dplyr,
    S4Vectors
Suggests: 
    testthat,
    knitr,
    rmarkdown
LinkingTo: 
    BH (>= 1.66.0),
    Rcpp (>= 1.0.0),
    RcppEigen (>= 0.3.3.3.0),
    RcppParallel (>= 5.0.1),
    rstan (>= 2.21.3),
    StanHeaders (>= 2.21.0.7)
SystemRequirements: GNU make
biocViews: ImmunoOncology, 
    DNAMethylation,
    DifferentialMethylation,
    DifferentialExpression,
	  GeneExpression,
	  GeneRegulation,
    Epigenetics,
	  Genetics,
	  Clustering,
	  FeatureExtraction,
	  Regression,
	  Bayesian,
	  Sequencing, 
	  Coverage,
	  SingleCell
VignetteBuilder: knitr