recipe bioconductor-sccb2

CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data






scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

package bioconductor-sccb2

(downloads) docker_bioconductor-sccb2



depends bioconductor-dropletutils:


depends bioconductor-edger:


depends bioconductor-rhdf5:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-doparallel:

depends r-foreach:

depends r-iterators:

depends r-matrix:

depends r-seurat:



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-sccb2

and update with::

   mamba update bioconductor-sccb2

To create a new environment, run:

mamba create --name myenvname bioconductor-sccb2

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-sccb2/tags`_ for valid values for ``<tag>``)

Download stats