{"id":19106735,"url":"https://github.com/bodenmillergroup/cytomapper","last_synced_at":"2025-07-19T08:11:55.303Z","repository":{"id":44922056,"uuid":"234525343","full_name":"BodenmillerGroup/cytomapper","owner":"BodenmillerGroup","description":"R package for visualization of highly multiplexed imaging 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src=\"vignettes/cytomapper_sticker.png\" align=\"right\" alt=\"\" width=\"100\" /\u003e\n\n# cytomapper\n\n\u003c!-- badges: start --\u003e\n\n[![docs](https://github.com/BodenmillerGroup/cytomapper/actions/workflows/docs.yml/badge.svg)](https://github.com/BodenmillerGroup/cytomapper/actions/workflows/docs.yml) [![codecov](https://codecov.io/gh/BodenmillerGroup/cytomapper/branch/devel/graph/badge.svg)](https://app.codecov.io/gh/BodenmillerGroup/cytomapper/tree/devel)\n\n\u003c!-- badges: end --\u003e\n\nR/Bioconductor package to spatially visualize pixel- and cell-level information obtained from highly multiplexed imaging.\n\nIts official package page can be found here: [https://bioconductor.org/packages/cytomapper](https://bioconductor.org/packages/cytomapper)\n\n## Check status\n\n| Bioc branch | Checks |\n|:-----------:|:------:|\n| Release     |[![build-check-release](https://github.com/BodenmillerGroup/cytomapper/workflows/build-checks-release/badge.svg)](https://github.com/BodenmillerGroup/cytomapper/actions?query=workflow%3Abuild-checks-release)|\n| Devel       |[![build-check-devel](https://github.com/BodenmillerGroup/cytomapper/workflows/build-checks-devel/badge.svg)](https://github.com/BodenmillerGroup/cytomapper/actions?query=workflow%3Abuild-checks-devel)|\n\n\n## Introduction\n\nHighly multiplexed imaging acquires single-cell expression values of selected proteins in a spatially-resolved fashion. \nThese measurements can be visualized across multiple length-scales. \nFirst, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. \nSecond, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualized on segmented cell areas. \nThis package contains functions for the visualization of multiplexed read-outs and cell-level information obtained by multiplexed imaging cytometry. \nThe main functions of this package allow 1. the visualization of pixel-level information across multiple channels (`plotPixels`), 2. the display of cell-level information (expression and/or metadata) on segmentation masks (`plotCells`) and 3. gating + visualization of cells on images (`cytomapperShiny`).\n\nThe `cytomapper` package provides toy data that were generated using imaging mass cytometry [1] taken from Damond _et al._ [2].\nFor further instructions to process raw imaging mass cytometry data, please refer to the [IMC Segmentation Pipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline) and the [histoCAT](https://github.com/BodenmillerGroup/histoCAT) as alternative visualization tool.\n\n## Requirements\n\nThe `cytomapper` package requires R version \u003e= 4.0.\nIt builds on data objects and functions contained in the [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) and [EBImage](https://bioconductor.org/packages/release/bioc/html/EBImage.html) packages.\nTherefore, these packages need to be installed (see below).\n\n## Installation\n\nThe `cytomapper` package can be installed from `Bioconductor` via:\n\n```r\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\n    \nBiocManager::install(\"cytomapper\")\n```\n\nThe development version of the `cytomapper` package can be installed from Github using `remotes` in R.\nPlease make sure to also install its dependecies:\n\n```r\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\n    \nBiocManager::install(c(\"EBImage\", \"SingleCellExperiment\"))\n\n# install.packages(\"remotes\")\n\nremotes::install_github(\"BodenmillerGroup/cytomapper\", build_vignettes = TRUE, dependencies = TRUE)\n```\n\nTo load the package in your R session, type the following:\n\n```r\nlibrary(cytomapper)\n```\n\n## Functionality\n\nThe `cytomapper` package offers three main functions: `plotPixels`, `plotCells` and `cytomapperShiny`.\n\n**plotPixels**\n\nThe function takes a `CytoImageList` object (available via the `cytomapper` package) containing multi-channel images representing pixel-level expression values and optionally a `CytoImageList` object containing segementation masks and a `SingleCellExperiment` object containing cell-level metadata.\n\nIt allows the visualization of pixel-level information of up to six channels and outlining cells based on cell-level metadata.\nTo see the full functionality in R type:\n\n```r\n?plotPixels\n```\n\n**plotCells**\n\nThis function takes a `CytoImageList` object containing segementation masks and a `SingleCellExperiment` object containing cell-level mean expression values and metadata information.\n\nIt allows the visualization of cell-level expression data and metadata information.\nTo see the full functionality in R type:\n\n```r\n?plotCells\n```\n\n**cytomapperShiny**\n\nThis Shiny application allows gating of cells based on their expression values and visualises selected cells on their corresponding images. \n\nIt requires at least a `SingleCellExperiment` as input and optionally `CytoImageList` objects containing segmentation masks and multi-channel images.\nFor full details, please refer to:\n\n```r\n?cytomapperShiny\n```\n\n## Getting help\n\nFor more information on processing imaging mass cytometry data, please refer to the [IMC Segmentation Pipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline). \nThis pipeline generates multi-channel tiff stacks containing the pixel-level expression values and segementation masks that can be used for the plotting functions in the `cytomapper` package.\n\nMore information on how to work with and generate a `SingleCellExperiment` object can be obtained from: [Orchestrating Single-Cell Analysis with Bioconductor](https://osca.bioconductor.org/data-infrastructure.html)\n\nAn extensive introduction to image analysis in R can be found at: [Introduction to EBImage](https://bioconductor.org/packages/release/bioc/vignettes/EBImage/inst/doc/EBImage-introduction.html)\n\nA full overview on the analysis workflow and functionality of the `cytomapper` package can be found by typing:\n\n```r\nvignette(\"cytomapper\")\n```\n\nFor common issues regarding the `cytomapper` package, please refer to the [wiki](https://github.com/BodenmillerGroup/cytomapper/wiki).\n\n## Demonstrations\n\nTo see example usage of the `cytomapper` package, please refer to its [publication repository](https://github.com/BodenmillerGroup/cytomapper_publication) and a number of [workshop demonstrations](https://github.com/BodenmillerGroup/cytomapper_demos).\n\n## Citation\n\nPlease cite `cytomapper` as:\n\n```\nNils Eling, Nicolas Damond, Tobias Hoch, Bernd Bodenmiller (2020). cytomapper: an R/Bioconductor package for visualization of highly\n  multiplexed imaging data. Bioinformatics, doi: 10.1093/bioinformatics/btaa1061\n```\n\n## Authors\n\n[Nils Eling](https://github.com/nilseling) nils.eling 'at' dqbm.uzh.ch\n\n[Nicolas Damond](https://github.com/ndamond)\n\n[Tobias Hoch](https://github.com/toobiwankenobi)\n\n## Maintainer\n\n[Lasse Meyer](https://github.com/lassedochreden)\n\n## References\n\n[1] [Giesen et al. (2014), Nature Methods, 11](https://www.nature.com/articles/nmeth.2869)\n\n[2] [Damond et al. (2019), Cell Metabolism, 29](https://www.cell.com/cell-metabolism/fulltext/S1550-4131(18)30691-0)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbodenmillergroup%2Fcytomapper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbodenmillergroup%2Fcytomapper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbodenmillergroup%2Fcytomapper/lists"}