Package Recipe 'bioconductor-bandits'

recipe bioconductor-bandits

BANDITS: Bayesian ANalysis of DIfferenTial Splicing

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/BANDITS.html
License:: GPL (>= 3)
Recipe:: /bioconductor-bandits/meta.yaml

BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.

package bioconductor-bandits¶

Versions:

1.26.0-0, 1.22.0-0, 1.18.1-0, 1.16.1-0, 1.14.0-1, 1.14.0-0, 1.10.0-2, 1.10.0-1, 1.10.0-0,

1.26.0-0, 1.22.0-0, 1.18.1-0, 1.16.1-0, 1.14.0-1, 1.14.0-0, 1.10.0-2, 1.10.0-1, 1.10.0-0, 1.8.0-0, 1.6.0-1, 1.6.0-0, 1.4.0-0, 1.2.0-0, 1.0.0-1

Depends:

on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-biocparallel >=1.44.0,<1.45.0a0
on bioconductor-drimseq >=1.38.0,<1.39.0
on bioconductor-drimseq >=1.38.0,<1.39.0a0
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-base >=4.5,<4.6.0a0
on r-data.table
on r-doparallel
on r-dorng
on r-foreach
on r-ggplot2
on r-mass
on r-r.utils
on r-rcpp
on r-rcpparmadillo

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-bandits

to add into an existing workspace instead, run:

pixi add bioconductor-bandits

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-bandits

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-bandits

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-bandits:<tag>

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