recipe bioconductor-tscan

Tools for Single-Cell Analysis







biotools: tscan, doi: 10.1093/nar/gkw430

Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented.

package bioconductor-tscan

(downloads) docker_bioconductor-tscan



depends bioconductor-delayedarray:


depends bioconductor-s4vectors:


depends bioconductor-singlecellexperiment:


depends bioconductor-summarizedexperiment:


depends bioconductor-trajectoryutils:


depends r-base:


depends r-combinat:

depends r-fastica:

depends r-ggplot2:

depends r-gplots:

depends r-igraph:

depends r-matrix:

depends r-mclust:

depends r-mgcv:

depends r-plyr:

depends r-shiny:



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-tscan

and update with::

   mamba update bioconductor-tscan

To create a new environment, run:

mamba create --name myenvname bioconductor-tscan

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

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