https://github.com/bodenmillergroup/cytomapper
R package for visualization of highly multiplexed imaging data
https://github.com/bodenmillergroup/cytomapper
bioimaging imaging-mass-cytometry single-cell spatial-analysis
Last synced: 7 months ago
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R package for visualization of highly multiplexed imaging data
- Host: GitHub
- URL: https://github.com/bodenmillergroup/cytomapper
- Owner: BodenmillerGroup
- Created: 2020-01-17T10:23:22.000Z (about 6 years ago)
- Default Branch: devel
- Last Pushed: 2024-06-25T08:05:27.000Z (over 1 year ago)
- Last Synced: 2025-04-09T22:44:33.885Z (11 months ago)
- Topics: bioimaging, imaging-mass-cytometry, single-cell, spatial-analysis
- Language: R
- Homepage: https://bodenmillergroup.github.io/cytomapper/
- Size: 208 MB
- Stars: 32
- Watchers: 9
- Forks: 9
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# cytomapper
[](https://github.com/BodenmillerGroup/cytomapper/actions/workflows/docs.yml) [](https://app.codecov.io/gh/BodenmillerGroup/cytomapper/tree/devel)
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](https://bioconductor.org/packages/cytomapper)
## Check status
| Bioc branch | Checks |
|:-----------:|:------:|
| Release |[](https://github.com/BodenmillerGroup/cytomapper/actions?query=workflow%3Abuild-checks-release)|
| Devel |[](https://github.com/BodenmillerGroup/cytomapper/actions?query=workflow%3Abuild-checks-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](https://github.com/BodenmillerGroup/ImcSegmentationPipeline) and the [histoCAT](https://github.com/BodenmillerGroup/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](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) and [EBImage](https://bioconductor.org/packages/release/bioc/html/EBImage.html) 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](https://github.com/BodenmillerGroup/ImcSegmentationPipeline).
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](https://osca.bioconductor.org/data-infrastructure.html)
An 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)
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](https://github.com/BodenmillerGroup/cytomapper/wiki).
## Demonstrations
To 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).
## 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](https://github.com/nilseling) nils.eling 'at' dqbm.uzh.ch
[Nicolas Damond](https://github.com/ndamond)
[Tobias Hoch](https://github.com/toobiwankenobi)
## Maintainer
[Lasse Meyer](https://github.com/lassedochreden)
## References
[1] [Giesen et al. (2014), Nature Methods, 11](https://www.nature.com/articles/nmeth.2869)
[2] [Damond et al. (2019), Cell Metabolism, 29](https://www.cell.com/cell-metabolism/fulltext/S1550-4131(18)30691-0)