R based analysis workflow for multiplexed imaging data

R workflow highlighting analyses approaches for multiplexed imaging data.

Scope

This workflow explains the use of common R/Bioconductor packages to pre-process and analyse single-cell data obtained from segmented multichannel images. While we use imaging mass cytometry (IMC) data as an example, the concepts presented here can be applied to images obtained by other technologies (e.g. CODEX, MIBI, mIF, CyCIF, etc.). The workflow can be largely divided into the following parts:

Preprocessing (reading in the data, spillover correction)
Image- and cell-level quality control, low-dimensional visualization
Sample/batch effect correction
Cell phenotyping via clustering or classification
Single-cell visualization
Image visualization
Spatial analyses

Update freeze

This workflow has been actively developed until December 2023. At that time we used the most recent (v.0.16.0) version of steinbock to process the example data. If you are having issues when using newer versions of steinbock please open an issue here.

Usage

To reproduce the analysis displayed at https://bodenmillergroup.github.io/IMCDataAnalysis/ clone the repository via:

git clone https://github.com/BodenmillerGroup/IMCDataAnalysis.git

For reproducibility purposes, we provide a Docker container here.

After installing Docker you can first pull the container via:

docker pull ghcr.io/bodenmillergroup/imcdataanalysis:latest

and then run the container:

docker run -v /path/to/IMCDataAnalysis:/home/rstudio/IMCDataAnalysis \
    -e PASSWORD=bioc -p 8787:8787  \
    ghcr.io/bodenmillergroup/imcdataanalysis:latest

Of note: it is recommended to use a date-tagged version of the container to ensure reproducibility. This can be done via:

docker pull ghcr.io/bodenmillergroup/imcdataanalysis:<year-month-date>

An RStudio server session can be accessed via a browser at localhost:8787 using Username: rstudio and Password: bioc.
Navigate to IMCDataAnalysis and open the IMCDataAnalysis.Rproj file.
Code in the individual files can now be executed or the whole workflow can be build by entering bookdown::render_book().

Feedback

We provide the workflow as an open-source resource. It does not mean that this workflow is tested on all possible datasets or biological questions and there exist multiple ways of analysing data. It is therefore recommended to check the results and question their biological interpretation.

If you notice an issue or missing information, please report an issue here. We also welcome contributions in form of pull requests or feature requests in form of issues. Have a look at the source code at:

https://github.com/BodenmillerGroup/IMCDataAnalysis

Contributing guidelines

For feature requests and bug reports, please raise an issue here.

For adding new content to the book please work inside the Docker container as explained above. You can fork the repository, add your changes and open a pull request. To add new libraries to the container please add them to the Dockerfile.

Maintainer

Daniel Schulz

Contributors

Nils Eling Vito Zanotelli
Daniel Schulz
Jonas Windhager
Michelle Daniel
Lasse Meyer

Citation

Please cite the following paper when using the presented workflow 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}
}

Funding

The work was funded by the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie Actions grant agreement No 892225 (N.E) and by the CRUK IMAXT Grand Challenge (J.W.).

BodenmillerGroup / IMCDataAnalysis

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