recipe bioconductor-scmet

Bayesian modelling of cell-to-cell DNA methylation heterogeneity

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/scMET.html

License:

GPL-3

Recipe:

/bioconductor-scmet/meta.yaml

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.

package bioconductor-scmet

Versions:

1.0.0-0

Depends:

• bioconductor-biocstyle >=2.26.0,<2.27.0

• bioconductor-s4vectors >=0.36.0,<0.37.0

• bioconductor-singlecellexperiment >=1.20.0,<1.21.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• libblas >=3.9.0,<4.0a0

• libgcc-ng >=12

• liblapack >=3.9.0,<4.0a0

• libstdcxx-ng >=12

• r-assertthat

• r-base >=4.2,<4.3.0a0

• r-bh >=1.66.0

• r-coda

• r-cowplot

• r-data.table

• r-dplyr

• r-ggplot2

• r-logitnorm

• r-mass

• r-matrix

• r-matrixstats

• r-rcpp >=1.0.0

• r-rcppeigen >=0.3.3.3.0

• r-rcppparallel >=5.0.1

• r-rstan >=2.21.3

• r-rstantools >=2.1.0

• r-stanheaders >=2.21.0.7

• r-vgam

• r-viridis

Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-scmet

and update with:

conda update bioconductor-scmet

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-scmet:<tag>

(see bioconductor-scmet/tags for valid values for <tag>)

Download stats

Link to this page

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