recipe bioconductor-neuca

NEUral network-based single-Cell Annotation tool






NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types.

package bioconductor-neuca

(downloads) docker_bioconductor-neuca



depends bioconductor-limma:


depends bioconductor-singlecellexperiment:


depends r-base:


depends r-e1071:

depends r-keras:



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-neuca

and update with::

   mamba update bioconductor-neuca

To create a new environment, run:

mamba create --name myenvname bioconductor-neuca

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

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