cytomapper
This package is for version 3.13 of Bioconductor; for the stable, up-to-date release version, see cytomapper.
Visualization of highly multiplexed imaging data in R
Bioconductor version: 3.13
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.
Author: Nils Eling [aut, cre] 
, Nicolas Damond [aut] , Tobias Hoch [ctb]
Maintainer: Nils Eling <nils.eling at dqbm.uzh.ch>
citation("cytomapper")):
Installation
To install this package, start R (version "4.1") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("cytomapper")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cytomapper")| On disk storage of images | HTML | R Script |
| Visualization of imaging cytometry data in R | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | DataImport, ImmunoOncology, MultipleComparison, Normalization, OneChannel, SingleCell, Software, TwoChannel |
| Version | 1.4.1 |
| In Bioconductor since | BioC 3.11 (R-4.0) (4 years) |
| License | GPL (>= 2) |
| Depends | R (>= 4.0), EBImage, SingleCellExperiment, methods |
| Imports | S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5 |
| System Requirements | |
| URL | https://github.com/BodenmillerGroup/cytomapper |
| Bug Reports | https://github.com/BodenmillerGroup/cytomapper/issues |
See More
| Suggests | BiocStyle, knitr, rmarkdown, markdown, testthat, shinytest |
| Linking To | |
| Enhances | |
| Depends On Me | imcdatasets |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | cytomapper_1.4.1.tar.gz |
| Windows Binary | cytomapper_1.4.1.zip |
| macOS 10.13 (High Sierra) | cytomapper_1.4.1.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/cytomapper |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/cytomapper |
| Bioc Package Browser | https://code.bioconductor.org/browse/cytomapper/ |
| Package Short Url | https://bioconductor.org/packages/cytomapper/ |
| Package Downloads Report | Download Stats |
| Old Source Packages for BioC 3.13 | Source Archive |