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<img src="vignettes/cytomapper_sticker.png" align="right" alt="" width="100" /> # cytomapper <!-- badges: start --> [![codecov](]( [![docs](]( <!-- badges: end --> R/Bioconductor package to spatially visualize pixel- and cell-level information obtained from highly multiplexed imaging. Its official package page can be found here: []( ## Check status | Bioc branch | Checks | |:-----------:|:------:| | Release |[![build-check-release](](| | Devel |[![build-check-devel](](| ## Introduction Highly multiplexed imaging acquires single-cell expression values of selected proteins in a spatially-resolved fashion. These measurements can be visualized 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 visualized on segmented cell areas. This package contains functions for the visualization of multiplexed read-outs and cell-level information obtained by multiplexed imaging cytometry. The 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`). The `cytomapper` package provides toy data that were generated using imaging mass cytometry [1] taken from Damond _et al._ [2]. For further instructions to process raw imaging mass cytometry data, please refer to the [IMC Segmentation Pipeline]( and the [histoCAT]( as alternative visualization tool. ## Requirements The `cytomapper` package requires R version >= 4.0. It builds on data objects and functions contained in the [SingleCellExperiment]( and [EBImage]( packages. Therefore, these packages need to be installed (see below). ## Installation The `cytomapper` package can be installed from `Bioconductor` via: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("cytomapper") ``` The development version of the `cytomapper` package can be installed from Github using `remotes` in R. Please make sure to also install its dependecies: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("EBImage", "SingleCellExperiment")) # install.packages("remotes") remotes::install_github("BodenmillerGroup/cytomapper", build_vignettes = TRUE, dependencies = TRUE) ``` To load the package in your R session, type the following: ```r library(cytomapper) ``` ## Functionality The `cytomapper` package offers three main functions: `plotPixels`, `plotCells` and `cytomapperShiny`. **plotPixels** The 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. It allows the visualization of pixel-level information of up to six channels and outlining cells based on cell-level metadata. To see the full functionality in R type: ```r ?plotPixels ``` **plotCells** This function takes a `CytoImageList` object containing segementation masks and a `SingleCellExperiment` object containing cell-level mean expression values and metadata information. It allows the visualization of cell-level expression data and metadata information. To see the full functionality in R type: ```r ?plotCells ``` **cytomapperShiny** This Shiny application allows gating of cells based on their expression values and visualises selected cells on their corresponding images. It requires at least a `SingleCellExperiment` as input and optionally `CytoImageList` objects containing segmentation masks and multi-channel images. For full details, please refer to: ```r ?cytomapperShiny ``` ## Getting help For more information on processing imaging mass cytometry data, please refer to the [IMC Segmentation Pipeline]( This 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. More information on how to work with and generate a `SingleCellExperiment` object can be obtained from: [Orchestrating Single-Cell Analysis with Bioconductor]( An extensive introduction to image analysis in R can be found at: [Introduction to EBImage]( A full overview on the analysis workflow and functionality of the `cytomapper` package can be found by typing: ```r vignette("cytomapper") ``` For common issues regarding the `cytomapper` package, please refer to the [wiki]( ## Demonstrations To see example usage of the `cytomapper` package, please refer to its [publication repository]( and a number of [workshop demonstrations]( ## Citation Please cite `cytomapper` as: ``` Nils Eling, Nicolas Damond, Tobias Hoch, Bernd Bodenmiller (2020). cytomapper: an R/Bioconductor package for visualization of highly multiplexed imaging data. Bioinformatics, doi: 10.1093/bioinformatics/btaa1061 ``` ## Authors [Nils Eling]( nils.eling 'at' [Nicolas Damond]( [Tobias Hoch]( ## Maintainer [Lasse Meyer]( ## References [1] [Giesen et al. (2014), Nature Methods, 11]( [2] [Damond et al. (2019), Cell Metabolism, 29](