recipe bioconductor-veloviz

VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories






VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories.

package bioconductor-veloviz

(downloads) docker_bioconductor-veloviz



depends libblas:


depends libgcc-ng:


depends liblapack:


depends libstdcxx-ng:


depends r-base:


depends r-igraph:

depends r-matrix:

depends r-mgcv:

depends r-rcpp:

depends r-rspectra:



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-veloviz

and update with::

   mamba update bioconductor-veloviz

To create a new environment, run:

mamba create --name myenvname bioconductor-veloviz

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

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