recipe bioconductor-tricycle

tricycle: Transferable Representation and Inference of cell cycle






The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference.

package bioconductor-tricycle

(downloads) docker_bioconductor-tricycle



depends bioconductor-annotationdbi:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-s4vectors:


depends bioconductor-scater:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-circular:

depends r-dplyr:

depends r-ggnewscale:

depends r-ggplot2:

depends r-rcolorbrewer:

depends r-scattermore:



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-tricycle

and update with::

   mamba update bioconductor-tricycle

To create a new environment, run:

mamba create --name myenvname bioconductor-tricycle

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

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