scMET
This is the development version of scMET; for the stable release version, see scMET.
Bayesian modelling of cell-to-cell DNA methylation heterogeneity
Bioconductor version: Development (3.18)
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("scMET")):
Installation
To install this package, start R (version "4.3") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
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 | ||
| NEWS | Text |
Details
| biocViews | Bayesian, Clustering, Coverage, DNAMethylation, DifferentialExpression, DifferentialMethylation, Epigenetics, FeatureExtraction, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, Regression, Sequencing, SingleCell, Software |
| Version | 1.3.0 |
| 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 |
| 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) |
| Suggests | testthat, knitr, rmarkdown |
| System Requirements | GNU make |
| Enhances | |
| URL | |
| Bug Reports | https://github.com/andreaskapou/scMET/issues |
See More
| 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_1.3.0.tar.gz |
| Windows Binary | scMET_1.3.0.zip |
| macOS Binary (x86_64) | scMET_1.3.0.tgz |
| macOS Binary (arm64) | scMET_1.3.0.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 |