_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-cytomapper 1.18.0
Propagated dependencies: r-viridis@0.6.5 r-svgpanzoom@0.3.4 r-svglite@2.1.3 r-summarizedexperiment@1.36.0 r-spatialexperiment@1.16.0 r-singlecellexperiment@1.28.1 r-shinydashboard@0.7.2 r-shiny@1.8.1 r-s4vectors@0.44.0 r-rhdf5@2.50.0 r-rcolorbrewer@1.1-3 r-raster@3.6-30 r-nnls@1.6 r-matrixstats@1.4.1 r-hdf5array@1.34.0 r-ggplot2@3.5.1 r-ggbeeswarm@0.7.2 r-ebimage@4.48.0 r-delayedarray@0.32.0 r-biocparallel@1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/BodenmillerGroup/cytomapper
Licenses: GPL 2+
Synopsis: Visualization of highly multiplexed imaging data in R
Description:

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.

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