recipe bioconductor-cardelino

Clone Identification from Single Cell Data






Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used.

package bioconductor-cardelino

(downloads) docker_bioconductor-cardelino



depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-ggtree:


depends bioconductor-s4vectors:


depends bioconductor-snpstats:


depends bioconductor-variantannotation:


depends r-base:


depends r-combinat:

depends r-ggplot2:

depends r-matrix:

depends r-matrixstats:

depends r-pheatmap:

depends r-vcfr:



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-cardelino

and update with::

   mamba update bioconductor-cardelino

To create a new environment, run:

mamba create --name myenvname bioconductor-cardelino

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

Download stats