PEERCE, PD-L1 Expression Estimation for Rare Cancer Entities, aims to enhance the decision quality of pathologists in the assessment of PD-L1 expression in rare cancer entities. Given a whole-slide image, it calculates the Tumor Proportion Score, which indicates the percentate of tumor cells which are PD-L1 positive.

The PEERCE project includes a series of scripts designed to preprocess data, train models, and predict scores relevant to PD-L1 expression in rare cancer entities. Below is a brief overview of each script and its purpose.

1. Tumor Patch Detector Training

   • Script Name: train_tumor_patch_detector.py
   • Description: Trains a model to detect tumor areas in patches. This script can be executed from the terminal. Upon completion, it outputs a dictionary (in pickle format) containing detailed training logs, such as loss and accuracy metrics over epochs. Additionally, the trained model's weights are saved in the specified output directory, facilitating further analysis, evaluation, or deployment.

2. Cell Type Detector Training

   • Script Name: train_cell_type_detector.py
   • Description: Trains a model to differentiate between PD-L1 positive tumor cells, PD-L1 negative tumor cells, and other cells within tumor patches.

• Preprocess Data for Cell Type Detection Training
  • Script Name: preprocess_cell_type_data.py
  • Description: Leverages trained generalist models to transform sparse cell type annotations into annotations suitable for training. Cellpose is used to identify cell instances and outline the nuclei, eliminating the need for manual annotation of each cell's outline. A DeepLIIF generative adverserial network is employed to isolate the hema components from heavily PD-L1 stained areas, thereby enhancing Cellpose's precision in identifying cell instances. This preprocessing step is required for preparing the dataset for accurate cell type detection training.

• Predict Tumor Proportion Score (TPS) for WSIs
  • Script Name: predict_tps_wsi.py
  • Description: Predicts the Tumor Proportion Score for a whole-slide image, integrating outputs from both the tumor patch and cell type detection model to provide a comprehensive PD-L1 expression score.

PEERCE has been developed and tested on Linux/Ubuntu 22.04 LTS. While PEERCE is designed to be cross-platform, running on a similar environment ensures the best compatibility and performance. Users on other systems may experience differences in execution or functionality.

• For TPS Inference: A minimum of 4GB of GPU memory is required.
• For Model Training: A minimum of 12GB GPU memory is recommended. Note that the specific GPU memory requirements can vary depending on the model type and batch size used during the training process.

To set up PEERCE for development or usage, follow these steps:

1. Clone this Repository

   git clone https://github.com/Kainmueller-Lab/PEERCE
   cd PEERCE

2. Create a new Python environment (Recommended)
   It is recommended to create a new environment using Python 3.8 (tested) or higher to avoid any compatibility issues with the dependencies. You can create a new environment using Conda or venv. For Conda, you can install the environment directly from the provided environment.yml file:

   Using Conda:

   conda env create -f environment.yml
   conda activate peerce_env

   If you prefer using pip to manage packages, you can install the dependencies from the requirements.txt file:

   pip install -r requirements.txt

3. Install the package

   pip install -e .
   

The functionality provided by PEERCE can be accessed in several ways, catering to different use cases and preferences.
Below are the methods to run the function to predicting the Tumor Proportion Score (TPS) from whole-slide images (WSIs).

1. Script Execution
   The scripts included in the PEERCE project, like predict_tps_wsi.py, can be run directly from the command line. This method is straightforward and allows for quick execution of tasks. For example:

   python predict_tps_wsi.py --ometiff_path path/to/example.ome.tiff --output_folder path/to/output/folder/

2. CLI Command
   PEERCE provides a Command Line Interface (CLI) to facilitate easy access to its functionalities. The CLI commands abstract the script executions and offer a user-friendly way to interact with PEERCE. To predict the TPS using the CLI:

   peerce predict_tps_wsi --ometiff_path path/to/example.ome.tiff --output_folder path/to/output/folder/

3. Jupyter Notebook Tutorial
   For those who prefer an interactive approach or wish to understand the process in greater detail, the predict_tps_wsi.ipynb notebook offers a comprehensive guide. This notebook not only allows you to run the TPS prediction but also provides further information and context about the process. You can find this notebook in the jupyter_notebook_tutorials folder within the repository.

4. Importing as a Module
   If you are developing a Python project and wish to integrate PEERCE's functionality directly, you can import the required functions into your Python scripts. For instance:

   from peerce.predict_tps_wsi import predict_tps_wsi
   
   # Usage example
   predict_tps_wsi(ometiff_path='path/to/example.ome.tiff', output_folder='path/to/output/folder/')

PEERCE is open-sourced under the MIT License. This license enables anyone to freely use, modify, and distribute the project, subject to the conditions outlined in the LICENSE file found in the repository.