recipe bioconductor-dcats

Differential Composition Analysis Transformed by a Similarity matrix






Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term.

package bioconductor-dcats

(downloads) docker_bioconductor-dcats



depends r-aod:

depends r-base:


depends r-e1071:

depends r-matrixstats:

depends r-mcmcpack:

depends r-robustbase:



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-dcats

and update with::

   mamba update bioconductor-dcats

To create a new environment, run:

mamba create --name myenvname bioconductor-dcats

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

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