recipe bioconductor-censcyt

Differential abundance analysis with a right censored covariate in high-dimensional cytometry






Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models.

package bioconductor-censcyt

(downloads) docker_bioconductor-censcyt



depends bioconductor-biocparallel:


depends bioconductor-diffcyt:


depends bioconductor-edger:


depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-broom.mixed:

depends r-dirmult:

depends r-dplyr:

depends r-fitdistrplus:

depends r-lme4:

depends r-magrittr:

depends r-mass:

depends r-mice:

depends r-multcomp:

depends r-purrr:

depends r-rlang:

depends r-stringr:

depends r-survival:

depends r-tibble:

depends r-tidyr:



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-censcyt

and update with::

   mamba update bioconductor-censcyt

To create a new environment, run:

mamba create --name myenvname bioconductor-censcyt

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

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