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Morpheus: Generating Therapeutic Strategies using Spatial Omics
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.
Features
- Self-Supervised Learning: Utilizes unlabeled spatial omics data to learn predictive models for T-cell infiltration.
- Counterfactual Reasoning: Generates minimal perturbations to the tumor environment, hypothesizing potential improvements in T-cell responses.
- Deep Learning Integration: Employs advanced neural network architectures tailored for high-dimensional omics data.
- Scalability: Designed to handle large datasets typical of spatial omics studies, enabling robust analysis across numerous patient samples.
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
Run the following in the command line
pip install morpheus-spatial
Tutorial
See example_notebook.ipynb
for a complete workflow on using Morpheus to generate therapeutic strategies.
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