scMET

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see scMET.

Bayesian modelling of cell-to-cell DNA methylation heterogeneity


Bioconductor version: 3.17

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.

Author: Andreas C. Kapourani [aut, cre] , John Riddell [ctb]

Maintainer: Andreas C. Kapourani <kapouranis.andreas at gmail.com>

Citation (from within R, enter citation("scMET")):

Installation

To install this package, start R (version "4.3") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("scMET")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("scMET")
scMET analysis using synthetic data HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews Bayesian, Clustering, Coverage, DNAMethylation, DifferentialExpression, DifferentialMethylation, Epigenetics, FeatureExtraction, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, Regression, Sequencing, SingleCell, Software
Version 1.2.0
In Bioconductor since BioC 3.16 (R-4.2) (1.5 years)
License GPL-3
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
System Requirements GNU make
URL
Bug Reports https://github.com/andreaskapou/scMET/issues
See More
Suggests testthat, knitr, rmarkdown
Linking To 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)
Enhances
Depends On Me
Imports Me
Suggests Me
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Build Report Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package scMET_1.2.0.tar.gz
Windows Binary scMET_1.2.0.zip
macOS Binary (x86_64) scMET_1.2.0.tgz
macOS Binary (arm64) scMET_1.2.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/scMET
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/scMET
Bioc Package Browser https://code.bioconductor.org/browse/scMET/
Package Short Url https://bioconductor.org/packages/scMET/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.17 Source Archive