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msiflow

★ 22 Open GitHub ↗

collection of mass spectrometry imaging workflows

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<h2 align="center">
automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis
</h2>

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msiFlow contains 7 snakemake workflows:

| task | description | workflow
| --- | --- | --- |
| pre-processing | generates a peak list from raw MALDI MSI data | `msi_preprocessing_flow` |
| registration | combines MSI & IFM including IFM pre-processing | `msi_if_registration_flow` |
| segmentation | dim. reduction and clustering of MSI data | `msi_segmentation_flow`|
| segmentation | thresholding-based segmentation of IFM markers | `if_segmentation_flow`|
| analysis & visualisation | molecular changes in different ROIs between two groups by statistical analysis | `region_group_analysis_flow` |
| analysis & visualisation | molecular signatures of ROIs by ML classification | `molecular_signatures_flow` |
| analysis & visualisation | molecular heterogeneity in ROIs by UMAP-based clustering | `molecular_heterogeneity_flow` |

msiFlow can be run via **Docker** or **locally** by using a graphical user interface (gui) or the command-line interface (cli):
1. The **Docker version** of msiFlow is intended for **easy-to-use** execution and does not require package installations.
2. The **local version** of msiFlow is intended for **development** and **advanced usage**.

## ⚙️ Installation (< 5 min)

### Docker Version

To use the Docker version of msiFlow, follow these instructions:
1. install Docker for your operating system from [here](https://docs.docker.com/get-docker/).
2. start Docker
3. get the Docker image 
   1. from DockerHub by running `docker pull phispa1812/msiflow_<interface>` in a terminal. In the command enter `gui` for `<interface>` to install the **graphical user interface** or `cli` to install the **command-line interface**.
   2. or build the Docker image by navigating to the root directory of msiFlow in a terminal (e.g. via `cd msiFlow`) and running `docker build -f streamlit/Dockerfile -t msiflow_gui .` to install the **graphical user interface** or by running `docker build -t msiflow_cli .` to install the **command-line interface**.

### Local Version (without Docker)

To use the local version of msiFlow, follow these instructions:
1. download or clone this repository if you have Git installed by running `git clone https://github.com/Immunodynamics-Engel-Lab/msiflow.git` in a terminal
2. navigate to the root directory of msiFlow (e.g. via `cd msiFlow`) in a terminal
3. environment/package installation
   1. if you have Anaconda installed, create an environment of msiFlow by running `conda env create --file msiflow_env.yaml`. This creates an environemnt with all required packages at once.
   2. if you want to use pure Python, download all packages by running `pip install -r requirements.txt`

**Note:** The [latest release](https://github.com/Immunodynamics-Engel-Lab/msiflow/releases/tag/v1.0.1) provides a stable version for local execution on Ubuntu.

## 🖥️ System requirements
### Hardware 
The hardware requirements depend on the data size. The [example data](https://doi.org/10.5281/zenodo.11913042) for most workflows (except MSI pre-processing and segmentation) can be processed on a computer with 8 GB RAM. The example data for MSI pre-processing and segmentation requires at least 120 GB RAM.
### Software 
All software dependencies and versions are listed in the [requirements.txt](https://github.com/Immunodynamics-Engel-Lab/msiflow/blob/main/requirements.txt) and can be installed via pip for local execution (see installation instructions above). The software has been tested on Ubuntu 20.04.6 LTS.

## 🚀 Run msiFlow

### Docker Version

To run the Docker version of msiFlow, follow these instructions:
  - start Docker
  - start the **graphical user interface** by running `docker run -v <path-to-data>:/home/user/msiflow/data -p 8501:8501 phispa1812/msiflow_gui` in a terminal and navigating to `localhost:8501` in a browser (e.g. Firefox)
  - or execute a workflow via the **command-line interface** by running `docker run -v <path-to-data-and-config>:/home/user/msiflow/data -e 'WORKFLOW=<workflow>' -e 'CORES=<number-of-cores>' phispa1812/msiflow_cli` in a terminal

**Important note:** don't change the field *input path* when using the gui or the *data* parameter in the configuration file when using the cli! This parameter is only intended for the local version of msiFlow.
In the Docker version the data path is specified in the corresponding command by `<path-to-data>` or `<path-to-data-and-config>`.

**Note for Windows users:** to use Docker properly on Windows, install [Docker Desktop with WSL2 integration](https://docs.docker.com/desktop/setup/install/windows-install/). Once configured, run msiFlow via Docker from WSL (Ubuntu). When mounting data, convert Windows paths (e.g. `D:\dir\data`) to WSL format (e.g. `/mnt/d/dir/data`) and avoid whitespaces in file paths.

### Local Version

To run msiFlow locally, follow these instructions:
- in a terminal navigate to the root directory of msiFlow (e.g. via `cd msiFlow`)
- if you have installed the packages in a conda environment, activate the environment via `conda activate msiflow_env`
- start the **graphical user interface** by running `streamlit run streamlit/home.py` in a terminal and navigating to `localhost:8501` in a browser (e.g. Firefox)
- or execute a workflow via the **command-line interface** by running `snakemake --snakefile <workflow>/Snakefile --cores <number-of-cores> --configfile <path-to-config>` in a terminal

In the commands above
- enter the name of the workflow to be executed for `<workflow>`. The possible workflows to select are listed in the table above.
- enter the path of your data folder for `<path-to-data>`
- enter the path of your data folder which includes a configuration file for `<path-to-data-and-config>`
- enter the max. number of cores to be used by msiFlow for `<number-of-cores>`. To provide all cores type *all*.
- enter the path of your configuration file for `<path-to-config>` when using the **local** version. 
- add `--resources mem_mb=<available_RAM_in_MB>` to specify the available RAM to prevent out-of-memory errors for MSI pre-processing (optional for local execution in the command-line)

### Workflow-specific information

Each workflow directory contains a **README** with detailed information on 
- how to **run** the workflow via the command-line interface
- how to structure and name your **input data** files for successful execution of msiFlow
- how to set the **configuration** parameters 
  - example *config.yaml* is provided in each directory
  - a description of all parameters is provided in the [wiki](https://github.com/Immunodynamics-Engel-Lab/msiflow/wiki)
- where and what **output** files are generated
- where to find **example data** to run the workflow and reproduce the results

## ▶️ Demo
A small dataset to demo msiFlow with the expected output files can be found [here](https://github.com/Immunodynamics-Engel-Lab/msiflow/tree/main/demo). 
You can use the script *run_demo.sh* to run the demo locally. 
The script applies registration, segmentation and feature extraction to identify lipidomic signatures of a specific marker (here Ly6G). 
Alternatively, you can use the Docker cli version of msiFlow to reproduce the results by following these instructions:
1. Get the Docker image `phispa1812/msiflow_cli` as described above in the installation instructions.
2. Download this repo to save the [demo data](https://github.com/Immunodynamics-Engel-Lab/msiflow/tree/main/demo/data) on your computer. You will need to specify the path where the demo data is stored in *path-to-demo-data* in the following commands. 
3. Use the command to run the Docker version of msiFlow in the command-line as described above to
   1. run `msi_if_registration_flow` with *path-to-demo-data/msi_if_registration* for `<path-to-data-and-config>`
   2. run `if_segmentation_flow` with *path-to-demo-data/if_segmentation* for `<path-to-data-and-config>`. Before running the workflow, copy the registered image *path-to-demo-data/msi_if_registration/registered/UPEC_12.tif* into *path-to-demo-data/if_segmentation*. 
   3. run `molecular_signatures_flow` with *path-to-demo-data/Ly6G_signatures* for `<path-to-data-and-config>`. Before running the workflow, copy the segmented image *path-to-demo-data/if_segmentation/segmented/UPEC_12.tif* into *path-to-demo-data/Ly6G_signatures/bin_imgs*. 
   
The expected runtime for processing the demo data with the applied workflows is about 5 minutes on a normal desktop computer.

## 📊 Example data
Generally all example data to test the workflows can be found on Zenodo:

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.11913042.svg)](https://doi.org/10.5281/zenodo.11913042)

## 📄 Paper
Check out the paper: [msiFlow: automated workflows for reproducible and scalable multimodal mass spectrometry imaging and microscopy data analysis](https://doi.org/10.1038/s41467-024-55306-7)

Published in *Nature Communications*, 2025.

## ✉️ Correspondence
[**Prof. Dr. Daniel R. Engel**](mailto:danielrobert.engel@uk-essen.de): Department of Immunodynamics, Institute of Experimental Immunology and Imaging, University Hospital Essen, Essen, Germany

http://www.immunodynamics.de

## 🔗 References
1. Veselkov, K., Sleeman, J., Claude, E. et al. BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology. Sci Rep 8, 4053 (2018). https://doi.org/10.1038/s41598-018-22499-z
2. Grélard, F., Legland, D., Fanuel, M. et al. Esmraldi: efficient methods for the fusion of mass spectrometry and magnetic resonance images. BMC Bioinformatics 22, 56 (2021). https://doi.org/10.1186/s12859-020-03954-z
3. imzy. imzy: A new reader/writer interface to imzML and other imaging mass spectrometry formats. GitHub repository (2022). https://github.com/vandeplaslab/imzy

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