morpheus-spatial 0.5.0

No project description provided

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.