recipe bioconductor-desingle

DEsingle for detecting three types of differential expression in single-cell RNA-seq data






DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions.

package bioconductor-desingle

(downloads) docker_bioconductor-desingle



depends bioconductor-biocparallel:


depends r-base:


depends r-bbmle:


depends r-gamlss:


depends r-mass:


depends r-matrix:


depends r-maxlik:


depends r-pscl:


depends r-vgam:




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-desingle

and update with::

   mamba update bioconductor-desingle

To create a new environment, run:

mamba create --name myenvname bioconductor-desingle

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

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