https://github.com/bodenmillergroup/imcdataanalysis
R based workflow for multiplexed imaging data
https://github.com/bodenmillergroup/imcdataanalysis
bioconductor image-analysis single-cell spatial-analysis
Last synced: 6 months ago
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R based workflow for multiplexed imaging data
- Host: GitHub
- URL: https://github.com/bodenmillergroup/imcdataanalysis
- Owner: BodenmillerGroup
- License: mit
- Created: 2020-10-23T08:18:35.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-02-12T14:59:40.000Z (about 2 years ago)
- Last Synced: 2025-01-03T03:10:17.237Z (about 1 year ago)
- Topics: bioconductor, image-analysis, single-cell, spatial-analysis
- Language: HTML
- Homepage: https://bodenmillergroup.github.io/IMCDataAnalysis/
- Size: 357 MB
- Stars: 28
- Watchers: 10
- Forks: 12
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://doi.org/10.5281/zenodo.6806448)
# R based analysis workflow for multiplexed imaging data
[](https://github.com/BodenmillerGroup/IMCDataAnalysis/actions/workflows/build.yml)
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:
1. Preprocessing (reading in the data, spillover correction)
2. Image- and cell-level quality control, low-dimensional visualization
3. Sample/batch effect correction
4. Cell phenotyping via clustering or classification
5. Single-cell visualization
6. Image visualization
7. 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](https://github.com/BodenmillerGroup/IMCDataAnalysis/issues).
## Usage
To reproduce the analysis displayed at [https://bodenmillergroup.github.io/IMCDataAnalysis/](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](https://github.com/BodenmillerGroup/IMCDataAnalysis/pkgs/container/imcdataanalysis).
1. After installing [Docker](https://docs.docker.com/get-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:
```
2. An RStudio server session can be accessed via a browser at `localhost:8787` using `Username: rstudio` and `Password: bioc`.
3. Navigate to `IMCDataAnalysis` and open the `IMCDataAnalysis.Rproj` file.
4. 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](https://github.com/BodenmillerGroup/IMCDataAnalysis/issues). 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](https://github.com/BodenmillerGroup/IMCDataAnalysis)
## Contributing guidelines
For feature requests and bug reports, please raise an issue [here](https://github.com/BodenmillerGroup/IMCDataAnalysis/issues).
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](Dockerfile).
## Maintainer
[Daniel Schulz](https://github.com/SchulzDan)
## Contributors
[Nils Eling](https://github.com/nilseling)
[Vito Zanotelli](https://github.com/votti)
[Daniel Schulz](https://github.com/SchulzDan)
[Jonas Windhager](https://github.com/jwindhager)
[Michelle Daniel](https://github.com/michdaniel)
[Lasse Meyer](https://github.com/lassedochreden)
## 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.).