Package Recipe 'bioconductor-scmet'

recipe bioconductor-scmet

Bayesian modelling of cell-to-cell DNA methylation heterogeneity

Homepage:: https://bioconductor.org/packages/3.20/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:

1.12.0-0, 1.8.0-1, 1.8.0-0, 1.4.0-0, 1.2.0-0, 1.0.0-1, 1.0.0-0

Depends:

on bioconductor-biocstyle >=2.38.0,<2.39.0
on bioconductor-biocstyle >=2.38.0,<2.39.0a0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-s4vectors >=0.48.0,<0.49.0a0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0
on bioconductor-singlecellexperiment >=1.32.0,<1.33.0a0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-assertthat
on r-base >=4.5,<4.6.0a0
on r-bh >=1.66.0
on r-coda
on r-cowplot
on r-data.table
on r-dplyr
on r-ggplot2
on r-logitnorm
on r-mass
on r-matrix
on r-matrixstats
on r-rcpp >=1.0.0
on r-rcppeigen >=0.3.3.3.0
on r-rcppparallel >=5.0.1
on r-rstan >=2.21.3
on r-rstantools >=2.1.0
on r-stanheaders >=2.21.0.7
on r-vgam
on r-viridis
on tbb-devel >=2022.3.0,<2022.4.0a0

Additional platforms:

linux-aarch64, osx-arm64

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-scmet

to add into an existing workspace instead, run:

pixi add bioconductor-scmet

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-scmet

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-scmet

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

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

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-scmet/README.html)