recipe bioconductor-roseq

Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data






ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used.

package bioconductor-roseq

(downloads) docker_bioconductor-roseq



depends bioconductor-edger:


depends bioconductor-limma:


depends r-base:


depends r-pbmcapply:



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-roseq

and update with::

   mamba update bioconductor-roseq

To create a new environment, run:

mamba create --name myenvname bioconductor-roseq

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

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