morpheus-spatial 0.10.0

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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, <3.12
• PyTorch Lightning 2.2.0 or higher
• CUDA 11.7 or higher (for GPU acceleration)
• Other dependencies listed in requirements.txt

Note numpy 2.0 or above not currently supported

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-spatial
pip install -r requirements.txt
pip install .

Tutorial

See tutorial_notebook.ipynb for a complete, self-contained workflow on using Morpheus to generate therapeutic strategies.

Known Issues

OpenMP Conflicts on macOS

Some users may encounter warnings about conflicting OpenMP libraries. If you see a warning about Intel OpenMP and LLVM OpenMP being loaded at the same time, try the following solution:

Set this environment variable before running your Python script or notebook:

export KMP_DUPLICATE_LIB_OK=TRUE

Alternatively, you can set this in your Python code:

import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'

If issues persist, please open an issue on our GitHub repository.

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.