CytoSummaryNet

This repository implements CytoSummaryNet the method described in "Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning" (van Dijk et al., 2024, PLOS Computational Biology).

CytoSummaryNet learns an optimal way to aggregate single-cell features into population-level profiles, outperforming traditional averaging on tasks like mechanism-of-action prediction.

Repository Structure

.
├── cytosummarynet/     # Core package implementation
└── paper_experiments/  # Code to reproduce paper results

Quick Start

Install the package:

pip install cytosummarynet

For detailed documentation:

• Core package usage: See cytosummarynet/README.md
• Reproducing paper results: See paper_experiments/README.md

Citation

If you use this code, please cite:

van Dijk, R., Arevalo, J., Babadi, B., Carpenter, A. E., & Singh, S. (2024).
Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning. PLOS Computational Biology.