recipe bioconductor-demuxsnp

scRNAseq demultiplexing using cell hashing and SNPs






This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework.

package bioconductor-demuxsnp

(downloads) docker_bioconductor-demuxsnp



depends bioconductor-biocgenerics:


depends bioconductor-demuxmix:


depends bioconductor-ensembldb:


depends bioconductor-genomeinfodb:


depends bioconductor-iranges:


depends bioconductor-matrixgenerics:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends bioconductor-variantannotation:


depends r-base:


depends r-class:

depends r-combinat:

depends r-matrix:



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-demuxsnp

and update with::

   mamba update bioconductor-demuxsnp

To create a new environment, run:

mamba create --name myenvname bioconductor-demuxsnp

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

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