Package Recipe 'bioconductor-dmcfb'

recipe bioconductor-dmcfb

Differentially Methylated Cytosines via a Bayesian Functional Approach

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/DMCFB.html
License:: GPL-3
Recipe:: /bioconductor-dmcfb/meta.yaml

DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.

package bioconductor-dmcfb¶

Versions:

1.24.0-0, 1.20.0-0, 1.14.0-0, 1.12.0-0, 1.8.0-0, 1.6.0-0, 1.4.0-1, 1.4.0-0, 1.2.0-0,

1.24.0-0, 1.20.0-0, 1.14.0-0, 1.12.0-0, 1.8.0-0, 1.6.0-0, 1.4.0-1, 1.4.0-0, 1.2.0-0, 1.0.0-0

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-rtracklayer >=1.70.0,<1.71.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on r-arm
on r-base >=4.5,<4.6.0a0
on r-benchmarkme
on r-data.table
on r-fastdummies
on r-mass
on r-matrixstats
on r-speedglm
on r-tibble

Additional platforms:

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-dmcfb

to add into an existing workspace instead, run:

pixi add bioconductor-dmcfb

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-dmcfb

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-dmcfb

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-dmcfb:<tag>

(see bioconductor-dmcfb/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-dmcfb/README.html)