recipe bioconductor-cdi

Clustering Deviation Index (CDI)



GPL-3 + file LICENSE



Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells.

package bioconductor-cdi

(downloads) docker_bioconductor-cdi



depends bioconductor-biocparallel:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-ggplot2:

depends r-ggsci:

depends r-matrixstats:

depends r-reshape2:

depends r-seurat:

depends r-seuratobject:



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-cdi

and update with::

   mamba update bioconductor-cdi

To create a new environment, run:

mamba create --name myenvname bioconductor-cdi

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

Download stats