recipe bioconductor-barcodetrackr

Functions for Analyzing Cellular Barcoding Data






barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies.

package bioconductor-barcodetrackr

(downloads) docker_bioconductor-barcodetrackr



depends bioconductor-s4vectors:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-circlize:

depends r-cowplot:

depends r-dplyr:

depends r-ggdendro:

depends r-ggplot2:

depends r-ggridges:

depends r-magrittr:

depends r-plyr:

depends r-proxy:

depends r-rcolorbrewer:

depends r-rlang:

depends r-scales:

depends r-shiny:

depends r-tibble:

depends r-tidyr:

depends r-vegan:

depends r-viridis:



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-barcodetrackr

and update with::

   mamba update bioconductor-barcodetrackr

To create a new environment, run:

mamba create --name myenvname bioconductor-barcodetrackr

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

Download stats