recipe bioconductor-mumosa

Multi-Modal Single-Cell Analysis Methods






Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities.

package bioconductor-mumosa

(downloads) docker_bioconductor-mumosa



depends bioconductor-batchelor:


depends bioconductor-beachmat:


depends bioconductor-biocgenerics:


depends bioconductor-biocneighbors:


depends bioconductor-biocparallel:


depends bioconductor-biocsingular:


depends bioconductor-delayedarray:


depends bioconductor-delayedmatrixstats:


depends bioconductor-iranges:


depends bioconductor-metapod:


depends bioconductor-s4vectors:


depends bioconductor-scaledmatrix:


depends bioconductor-scran:


depends bioconductor-scuttle:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-igraph:

depends r-matrix:

depends r-uwot:



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-mumosa

and update with::

   mamba update bioconductor-mumosa

To create a new environment, run:

mamba create --name myenvname bioconductor-mumosa

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

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