recipe r-bulkanalyser

Given an expression matrix from a bulk sequencing experiment, pre-processes it and creates a shiny app for interactive data analysis and visualisation. The app contains quality checks, differential expression analysis, volcano and cross plots, enrichment analysis and gene regulatory network inference, and can be customised to contain more panels by the user.



GPL2 / GPL-2.0-only



package r-bulkanalyser

(downloads) docker_r-bulkanalyser



depends bioconductor-complexheatmap:

depends bioconductor-deseq2:

depends bioconductor-edger:

depends bioconductor-genie3:

depends bioconductor-preprocesscore:

depends r-base:


depends r-circlize:

depends r-dplyr:

depends r-dt:

depends r-ggforce:

depends r-ggnewscale:

depends r-ggplot2:

depends r-ggrastr:

depends r-ggrepel:

depends r-ggvenndiagram:

depends r-glue:

depends r-gprofiler2:

depends r-magrittr:

depends r-matrixstats:

depends r-noisyr:

depends r-rcolorbrewer:

depends r-rlang:

depends r-scales:

depends r-shiny:

depends r-shinyjqui:

depends r-shinyjs:

depends r-shinylp:

depends r-shinywidgets:

depends r-stringr:

depends r-tibble:

depends r-tidyr:

depends r-upsetr:

depends r-visnetwork:



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 r-bulkanalyser

and update with::

   mamba update r-bulkanalyser

To create a new environment, run:

mamba create --name myenvname r-bulkanalyser

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

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