recipe bioconductor-caen

Category encoding method for selecting feature genes for the classification of single-cell RNA-seq






With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.

package bioconductor-caen

(downloads) docker_bioconductor-caen



depends bioconductor-summarizedexperiment:


depends r-base:


depends r-poiclaclu:



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-caen

and update with::

   mamba update bioconductor-caen

To create a new environment, run:

mamba create --name myenvname bioconductor-caen

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

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