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https://github.com/bodenmillergroup/imcrtools

An R package for handling and analysing imaging mass cytometry data
https://github.com/bodenmillergroup/imcrtools

imc r single-cell spatial

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An R package for handling and analysing imaging mass cytometry data

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README 

        



# imcRtools



[![codecov](https://codecov.io/gh/BodenmillerGroup/imcRtools/branch/master/graph/badge.svg)](https://codecov.io/gh/BodenmillerGroup/imcRtools)

[![docs](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/docs.yml/badge.svg?branch=master)](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/docs.yml)



This R/Bioconductor package contains helper functions to analyse IMC (or other multiplexed imaging) data.



Its official package page can be found here: [https://bioconductor.org/packages/imcRtools](https://bioconductor.org/packages/imcRtools)



**Bug notice: we discovered and fixed a bug in the `testInteractions` function in version below 1.5.5 which affected `SingleCellExperiment` or `SpatialExperiment` objects in which cells were not grouped by image. Please install the newest version of `imcRtools` directly from Github as explained below.**  



## Check status



| Bioc branch | Checks |

|:-----------:|:------:|

| Release     |[![build-check-release](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-release.yml/badge.svg?branch=devel)](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-release.yml)|

| Devel       |[![build-check-devel](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-devel.yml/badge.svg?branch=devel)](https://github.com/BodenmillerGroup/imcRtools/actions/workflows/build-checks-devel.yml)|



## Introduction



Highly multiplexed imaging techniques such as imaging mass cytometry (IMC), 

multiplexed ion beam imaging (MIBI) and cyclic immunofluorescence techniques

acquire read-outs of the expression of tens of protein in a spatially resolved

manner.



This R package supports the handling and analysis of imaging mass cytometry 

and other highly multiplexed imaging data. The main functionality includes 

reading in single-cell data after image segmentation and measurement, data 

formatting to perform channel spillover correction and a number of spatial 

analysis approaches. First, cell-cell interactions are detected via spatial 

graph construction; these graphs can be visualized with cells representing 

nodes and interactions representing edges. Furthermore, per cell, its direct 

neighbours are summarized to allow spatial clustering. Per image/grouping 

level, interactions between types of cells are counted, averaged and 

compared against random permutations. In that way, types of cells that 

interact more (attraction) or less (avoidance) frequently than expected by 

chance are detected. 



## Installation



The `imcRtools` package can be installed from `Bioconductor` via:



```r

if (!requireNamespace("BiocManager", quietly = TRUE))

    install.packages("BiocManager")



BiocManager::install("imcRtools")

```



The development version of `imcRtools` can be installed from Github via:



```r

if (!requireNamespace("remotes", quietly = TRUE))

    install.packages("remotes")



remotes::install_github("BodenmillerGroup/imcRtools")

```



## Getting help



The analysis of highly multiplexed imaging data requires multiple pre-processing

and diverse analysis steps.



1. Processing of raw data and segmentation: The 

[ImcSegmentationPipeline](https://github.com/BodenmillerGroup/ImcSegmentationPipeline) and 

the [steinbock](https://github.com/BodenmillerGroup/steinbock) 

library can be used to process and segment IMC data. The

`imcRtools` package provides reader functions for outputs generated by these 

approaches.  



2. Single-cell analysis using the [Bioconductor](https://www.bioconductor.org/) framework: The 

[Orchestrating Single-Cell Analysis with Bioconductor](https://bioconductor.org/books/release/OSCA/)

book is an excellent resource for beginners and advanced analysis concerning

single-cell data. Common analysis steps include dimensionality reduction, 

unsupervised clustering for cell type detection and data visualization.

The `imcRtools` package supports data structures that fully

integrate with the analysis presented in the OSCA book.  



3. Handling multiplexed images in R: the 

[cytomapper](https://www.bioconductor.org/packages/release/bioc/html/cytomapper.html)

Bioconductor package provides functions and data structure to handle and 

analyse highly multiplexed imaging data (images, masks and single-cell data)

natively in R.



## Citation



Please cite the following paper when using `imcRtools` in your research:



>  Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00881-0



    @article{Windhager2023,

        author = {Windhager, Jonas and Zanotelli, Vito R.T. and Schulz, Daniel and Meyer, Lasse and Daniel, Michelle and Bodenmiller, Bernd and Eling, Nils},

        title = {An end-to-end workflow for multiplexed image processing and analysis},

        year = {2023},

        doi = {10.1038/s41596-023-00881-0},

        URL = {https://www.nature.com/articles/s41596-023-00881-0},

        journal = {Nature Protocols}

    }



## Contributing



For feature requests, please open an issue [here](https://github.com/BodenmillerGroup/imcRtools/issues).



Alternatively, you can fork the repository, add your change and issue a pull request.



## Maintainer



**Daniel Schulz**