vmrseq
Probabilistic Modeling of Single-cell Methylation Heterogeneity
Bioconductor version: 3.24 · Package version: 1.5.1
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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 |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | DNAMethylation, Epigenetics, ImmunoOncology, Sequencing, SingleCell, Software, WholeGenome |
| Package Short Url | https://bioconductor.org/packages/vmrseq/ |
Citation
From within R, enter citation("vmrseq"):
Keegan Korthauer, Ning Shen. vmrseq: Probabilistic Modeling of Single-cell Methylation Heterogeneity. doi:10.18129/B9.bioc.vmrseq, R package version 1.5.1, https://bioconductor.org/packages/vmrseq.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
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)