recipe bioconductor-messina

Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems



EPL (>= 1.0)




biotools: messina

Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.

package bioconductor-messina

(downloads) docker_bioconductor-messina



depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-foreach:


depends r-ggplot2:


depends r-plyr:


depends r-rcpp:


depends r-survival:




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-messina

and update with::

   mamba update bioconductor-messina

To create a new environment, run:

mamba create --name myenvname bioconductor-messina

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-messina/tags`_ for valid values for ``<tag>``)

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