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cytomapper

Visualization of highly multiplexed imaging data in R

Bioconductor version: 3.23 · Package version: 1.24.0

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("cytomapper")

Details

MaintainerLasse Meyer <lasse.meyer@dqbm.uzh.ch>
AuthorNils Eling [aut] (ORCID: <https://orcid.org/0000-0002-4711-1176>), Nicolas Damond [aut] (ORCID: <https://orcid.org/0000-0003-3027-8989>), Tobias Hoch [ctb], Lasse Meyer [cre, ctb] (ORCID: <https://orcid.org/0000-0002-1660-1199>)
LicenseGPL (>= 2)
URLhttps://github.com/BodenmillerGroup/cytomapper
Bug Reportshttps://github.com/BodenmillerGroup/cytomapper/issues
Downloads rank654
Source branchRELEASE_3_23
biocViewsDataImport, ImmunoOncology, MultipleComparison, Normalization, OneChannel, SingleCell, Software, TwoChannel

Documentation

Download

Dependencies

Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods

Imports: SpatialExperiment, S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5, nnls

Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest

Reverse dependencies

Depends On Me (1): imcdatasets

Imports Me (3): cytoviewer, imcRtools, simpleSeg

Suggests Me (1): SpatialDatasets