vmrseq

Probabilistic Modeling of Single-cell Methylation Heterogeneity

Bioconductor version: 3.24 · Package version: 1.5.1

High-throughput single-cell measurements of DNA methylation allows studying inter-cellular epigenetic heterogeneity, but this task faces the challenges of sparsity and noise. We present vmrseq, a statistical method that overcomes these challenges and identifies variably methylated regions accurately and robustly.

Installation

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

BiocManager::install("vmrseq")

Details

Maintainer	Keegan Korthauer <kkorthauer@cmmt.ubc.ca>
Author	Keegan Korthauer [aut, cre] (ORCID: <https://orcid.org/0000-0002-4565-1654>), Ning Shen [aut] (ORCID: <https://orcid.org/0000-0002-2974-1086>)
License	MIT + file LICENSE
URL	https://github.com/nshen7/vmrseq
Bug Reports	https://github.com/nshen7/vmrseq/issues
Downloads rank	316
Source branch	devel
biocViews	DNAMethylation, Epigenetics, ImmunoOncology, Sequencing, SingleCell, Software, WholeGenome

Documentation

Analyzing single-cell bisulfite sequencing data with vmrseq

Download

Follow the installation instructions to use this package in your R session.

Source package	vmrseq_1.5.1.tar.gz
Windows binary (x86_64)	vmrseq_1.5.1.zip
macOS binary (arm64)	vmrseq_1.5.1.tgz
macOS binary (x86_64)	vmrseq_1.5.1.tgz

Dependencies

Depends: R (>= 4.5.0)

Imports: bumphunter, dplyr, BiocParallel, DelayedArray, GenomicRanges, ggplot2, methods, tidyr, locfit, gamlss.dist, recommenderlab, HDF5Array, data.table, SummarizedExperiment, IRanges, S4Vectors, devtools

Suggests: knitr, rmarkdown, testthat (>= 3.0.0)