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cytomapper

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R/Bioconductor package to spatially visualize pixel- and cell-level information obtained from highly multiplexed imaging.

Its official package page can be found here: https://bioconductor.org/packages/cytomapper

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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:

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:

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:

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:

?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:

?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:

?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:

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' dqbm.uzh.ch

Nicolas Damond

Tobias Hoch

References

[1] Giesen et al. (2014), Nature Methods, 11

[2] Damond et al. (2019), Cell Metabolism, 29