recipe bioconductor-cytomapper

Visualization of highly multiplexed imaging data in R

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/cytomapper.html

License:

GPL (>= 2)

Recipe:

/bioconductor-cytomapper/meta.yaml

Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.

package bioconductor-cytomapper

versions:

1.14.0-0,  1.12.0-0,  1.10.0-0,  1.6.0-0,  1.4.1-0,  1.2.1-0,  1.2.0-0,  1.0.0-0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0

depends bioconductor-delayedarray:

>=0.28.0,<0.29.0

depends bioconductor-ebimage:

>=4.44.0,<4.45.0

depends bioconductor-hdf5array:

>=1.30.0,<1.31.0

depends bioconductor-rhdf5:

>=2.46.0,<2.47.0

depends bioconductor-s4vectors:

>=0.40.0,<0.41.0

depends bioconductor-singlecellexperiment:

>=1.24.0,<1.25.0

depends bioconductor-spatialexperiment:

>=1.12.0,<1.13.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends r-base:

>=4.3,<4.4.0a0

depends r-ggbeeswarm:

depends r-ggplot2:

depends r-matrixstats:

depends r-nnls:

depends r-raster:

depends r-rcolorbrewer:

depends r-shiny:

depends r-shinydashboard:

depends r-svglite:

depends r-svgpanzoom:

depends r-viridis:

requirements:

additional platforms:

Installation

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-cytomapper

and update with::

   mamba update bioconductor-cytomapper

To create a new environment, run:

mamba create --name myenvname bioconductor-cytomapper

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 quay.io/biocontainers/bioconductor-cytomapper:<tag>

(see `bioconductor-cytomapper/tags`_ for valid values for ``<tag>``)

Download stats

Link to this page

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