recipe bioconductor-missrows

Handling Missing Individuals in Multi-Omics Data Integration






The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values.

package bioconductor-missrows

(downloads) docker_bioconductor-missrows



depends bioconductor-multiassayexperiment:


depends bioconductor-s4vectors:


depends r-base:


depends r-ggplot2:

depends r-gtools:

depends r-plyr:



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-missrows

and update with::

   mamba update bioconductor-missrows

To create a new environment, run:

mamba create --name myenvname bioconductor-missrows

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

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