recipe bioconductor-granulator

Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data






granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.

package bioconductor-granulator

(downloads) docker_bioconductor-granulator



depends r-base:


depends r-cowplot:

depends r-dplyr:

depends r-dtangle:

depends r-e1071:

depends r-epir:

depends r-ggplot2:

depends r-ggplotify:

depends r-limsolve:

depends r-magrittr:

depends r-mass:

depends r-nnls:

depends r-pheatmap:

depends r-purrr:

depends r-rlang:

depends r-tibble:

depends r-tidyr:



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-granulator

and update with::

   mamba update bioconductor-granulator

To create a new environment, run:

mamba create --name myenvname bioconductor-granulator

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

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