Deep learning-based analysis reveals patient-level cancer therapy trajectories using single-cell PBMC chromatin images

This repository contains code for the paper "Deep learning-based analysis reveals patient-level cancer therapy trajectories using single-cell PBMC chromatin images" which analyzes PBMC chromatin images from 5 timepoints from patients undergoing proton radiation therapy and healthy volunteers to create patient trajectories and associate these with therapy outcomes.

Data

The dataset used in this project can be downloaded at TODO.

Repository overview

• notebooks contains jupyter notebooks for segmenting and pre-processing the dataset and training models used in the paper's results. See notebooks/README.md for further details.
• figure_notebooks contains jupyter notebooks to reproduce the paper's main and supplementary figures. See figure_notebooks/README.md for further details.
• scripts contains scripts for randomizing the plate layouts and extracting chrometric features from the pre-processed images. See scripts/README.md for further details.
• meta contains select metadata needed for the figures and plate layout generation.

Dependencies:

Python:

This repository was developed using Python 3.9. You can use Conda to create a virtual environment for a specific Python version. Additional required packages are listed in requirements.txt and can be installed using the following command:

pip install -r requirements.txt

Installing dependencies can take a few minutes or up to an hour dependending on how many packages need to be downloaded rather than reusing cached versions.

Operating system and hardware:

We developed this code on a machine running Rocky Linux 8.8 (Green Obsidian) and equipped with an NVIDIA RTX A6000 GPU.

Citation

TODO