recipe bioconductor-hippo

Heterogeneity-Induced Pre-Processing tOol



GPL (>=2)



For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering.

package bioconductor-hippo

(downloads) docker_bioconductor-hippo



depends bioconductor-singlecellexperiment:


depends r-base:


depends r-dplyr:

depends r-ggplot2:

depends r-ggrepel:

depends r-gridextra:

depends r-irlba:

depends r-magrittr:

depends r-matrix:

depends r-reshape2:

depends r-rlang:

depends r-rtsne:

depends r-umap:



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-hippo

and update with::

   mamba update bioconductor-hippo

To create a new environment, run:

mamba create --name myenvname bioconductor-hippo

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

Download stats