In order to be able to use the segmentation, spillover compensation and background correction within MapMetIP, the fine-tuned cellpose models, spillover measurements and ilastik-trained background/foreground classifiers have to be downloaded from zenodo.

Replace path/to/extract/directory with the absolute path to the directory, where the data should be stored.

wget -P <path/to/extract/directory> https://zenodo.org/records/13220635/files/MapMetIP_models.zip
unzip <path/to/extract/directory>/MapMetIP_models.zip -d <path/to/extract/directory>
rm <path/to/extract/directory>/MapMetIP_models.zip

We prepared a small test dataset with one representative primary tumor and bone marrow sample to be used in the notebooks for demonstration purposes. Replace path/to/extract/directory with the absolute path to the directory, where the data should be stored.

wget -P <path/to/extract/directory> https://zenodo.org/records/13220635/files/MapMetIP_TestDataset.zip
unzip <path/to/extract/directory>/MapMetIP_TestDataset.zip -d <path/to/extract/directory>
rm <path/to/extract/directory>/MapMetIP_TestDataset.zip

The entire dataset containing all IF and IMC images from Lazic et al. has a size of ~69 GB (even after h5 compression with compression level 7). For download, replace path/to/extract/directory with the absolute path to the directory, where the data should be stored.

wget -P <path/to/extract/directory> https://zenodo.org/records/13220635/files/MapMetIP_FullDataset.zip
unzip <path/to/extract/directory>/MapMetIP_FullDataset.zip -d <path/to/extract/directory>
rm <path/to/extract/directory>/MapMetIP_FullDataset.zip

git clone https://github.com/TaschnerMandlGroup/MapMetIP.git

Build the docker image.

cd MapMetIP
docker build -t mapmet_ip .

The docker-based implementation assumes that the R-based docker image for spillover compensation was pulled from docker hub.

docker image pull lazdaria/spillovercomp

Then start the mapmet_ip container, mounting

• the Docker daemon socket (-v /var/run/docker.sock:/var/run/docker.sock) to ensure that the the R-based docker container for spillover compensation can be started from within
• the MapMetIP project directory (-v "$(pwd)":/usr/src/app/MapMetIP) and
• the path to the downloaded data (-v <path/to/extract/directory>:/data)

and allowing access to GPUs on host (--gpus all).

The R-based docker container is launched by the host's Docker daemon, therefore make sure to provide the absolute path to the downloaded data (-e "DOODPATH=<path/to/extract/directory>").

docker run -p 8888:8888 -v /var/run/docker.sock:/var/run/docker.sock -v "$(pwd)":/usr/src/app/MapMetIP  -v <path/to/extract/directory>:/data --gpus all -e "DOODPATH=<path/to/extract/directory>" -it mapmet_ip

A Jupyter Notebook server session can then be accessed via your browser at localhost:8888. The stdout of the started container will provide a token, which has to be copied for login.

git clone https://github.com/TaschnerMandlGroup/MapMetIP.git

It is recommended to install MapMetIP into a conda environment together with other necessary packages. If you are new to conda, please refer to these instructions first.

cd MapMetIP
conda env create -f env.yml

You can then activate the environment:

conda activate mapmet_ip

And install MapMetIP

pip install -e .

Then pull R-based image for spillover compensation:

docker image pull lazdaria/spillovercomp

To be able to use DIMR hot-poxel removal, clone the IMC-Denoise github repository to the parent directory of MapMetIP.

cd ..
git clone --branch v1.0.0 https://github.com/PENGLU-WashU/IMC_Denoise.git

In case problems with Tensorflow versions, occur, add the path to the IMC_Denoise parent directory to your ~/.bashrc:

export PYTHONPATH="${PYTHONPATH}:$(pwd)"

Notebooks, demonstrating each step of the pipeline on the primary tumor sample are provided:

• Demonstration of pipeline on one representative tumor sample (tests/process_TU_sample.ipynb)
• Demonstration of pipeline on one representative bone marrow sample(tests/process_BM_sample.ipynb)

First, make sure the conda environment is activated.

conda activate mapmet_ip

To run the complete image processing pipeline on a defined sample, use the command below.

cd MapMetIP
python3 run_all.py -s <sample_name> --data_path <path/to>/MapMetIP_TestDataset --model_path <path/to>/MapMetIP_models --save_dir <path/to/save/results> --log_path <path/to/save/logs>

To run the complete image processing pipeline on a list of samples, use the command below.

cd MapMetIP
python3 run_all.py -s <sample_name1> <sample_name2> <sample_name3> --data_path <path/to>/MapMetIP_TestDataset --model_path <path/to>/MapMetIP_models --save_dir <path/to/save/results> --log_path <path/to/save/logs>