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Project description
Morpheus: Generating Therapeutic Strategies from Multiplexed Tissue Images
Introduction
Morpheus is an integrated deep learning framework that takes large scale spatial omics profiles of patient tumors, and combines a formulation of T-cell infiltration prediction as a self-supervised machine learning problem with a counterfactual optimization strategy to generate minimal tumor perturbations predicted to boost T-cell infiltration.
Getting Started
Prerequisites
- Python 3.9 or higher
- PyTorch Lightning 2.2.0 or higher
- CUDA 11.7 or higher (for GPU acceleration)
- Other dependencies listed in
requirements.txt
Installation
Using pip (PyPI)
Run the following in the command line
pip install morpheus-spatial
From Source
To install Morpheus from source, clone the repository and install the dependencies:
git clone https://github.com/neonine2/morpheus-spatial.git
cd morpheus
pip install -r requirements.txt
pip install .
Tutorial
See tutorial_notebook.ipynb
for a complete workflow on using Morpheus to generate therapeutic strategies.
Repository Structure
assets/
: Contains images and other assets used in the documentation and the project.examples/
: Example scripts and notebooks demonstrating various use cases of the Morpheus framework.tutorial.ipynb
: A notebook demonstrating how to reproduce the primary analyses of the paper.
reproduction/
: Includes Jupyter notebooks and scripts for reproducing the main analyses presented in the associated research paper.reproduction_notebook.ipynb
: A notebook demonstrating how to reproduce the primary analyses of the paper.
src/
: The main package directory containing all core modules and functions.tests/
: Contains unit tests for the different modules of the package.requirements.txt
: A file listing all Python dependencies required to run the project.
Project details
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