recipe bioconductor-bayesknockdown

BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data






A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data.

package bioconductor-bayesknockdown

(downloads) docker_bioconductor-bayesknockdown



depends bioconductor-biobase:


depends r-base:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-bayesknockdown

and update with::

   mamba update bioconductor-bayesknockdown

To create a new environment, run:

mamba create --name myenvname bioconductor-bayesknockdown

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-bayesknockdown/tags`_ for valid values for ``<tag>``)

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