scMET

DOI: 10.18129/B9.bioc.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>

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")

 

HTML	R Script	scMET analysis using synthetic data
PDF		Reference Manual
Text		NEWS

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 year)
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
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)
Suggests	testthat, knitr, rmarkdown
SystemRequirements	GNU make
Enhances
URL
BugReports	https://github.com/andreaskapou/scMET/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
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