cytomapper: Visualization of highly multiplexed imaging data in R

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 details

AuthorNils Eling [aut, cre] (<https://orcid.org/0000-0002-4711-1176>), Nicolas Damond [aut] (<https://orcid.org/0000-0003-3027-8989>), Tobias Hoch [ctb]
Bioconductor views DataImport ImmunoOncology MultipleComparison Normalization OneChannel SingleCell Software TwoChannel
MaintainerNils Eling <nils.eling@dqbm.uzh.ch>
LicenseGPL (>= 2)
Version1.2.1
URL https://github.com/BodenmillerGroup/cytomapper
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("cytomapper")

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cytomapper documentation built on Jan. 30, 2021, 2:01 a.m.