recipe bioconductor-ctsv

Identification of cell-type-specific spatially variable genes accounting for excess zeros






The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS.

package bioconductor-ctsv

(downloads) docker_bioconductor-ctsv



depends bioconductor-biocparallel:


depends bioconductor-qvalue:


depends bioconductor-spatialexperiment:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-knitr:

depends r-pscl:



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-ctsv

and update with::

   mamba update bioconductor-ctsv

To create a new environment, run:

mamba create --name myenvname bioconductor-ctsv

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

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