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scDFM: Distributional Flow Matching for Robust Single-Cell Perturbation Prediction (ICLR 2026)

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# scDFM: Distributional Flow Matching for Robust Single-Cell Perturbation Prediction (ICLR 2026)

[![arXiv](https://img.shields.io/badge/arXiv-2601.01829-b31b1b?logo=arxiv)](https://arxiv.org/abs/2602.07103)
[![OpenReview](https://img.shields.io/badge/OpenReview-Forum-2D7FF9?logo=openreview&logoColor=white)](https://openreview.net/forum?id=QSGanMEcUV)
[![Codebase](https://img.shields.io/badge/Codebase-GitHub-181717?logo=github)](https://github.com/AI4Science-WestlakeU/scDFM)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow?logo=open-source-initiative&logoColor=white)](https://raw.githubusercontent.com/AI4Science-WestlakeU/scDFM/main/LICENSE)
[![YouTube](https://img.shields.io/badge/YouTube-Video-FF0000?logo=youtube&logoColor=white)](https://youtu.be/T4vNEsp7eLs)
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Official repo for the paper [scDFM](URL), ICLR 2026. 
Chenglei Yu∗1,2, [Chuanrui Wang](https://wang-cr.github.io/)∗1, Bangyan Liao1,2 & [Tailin Wu](https://tailin.org/)†1.

1School of Engineering, Westlake University; 
2Zhejaing University;

*Equal contribution, †Corresponding authors

----

## Overview 
We propose a novel distributional flow matching framework (scDFM) for robust single-cell perturbation prediction, which models the full distribution of perturbed cellular expression profiles conditioned on control states, thereby overcoming limitations of existing methods that rely on cell-level correspondences and fail to capture population-level transcriptional shifts.

Framework of paper:

## Install dependencies 
```
conda env create -f environment.yml
```

##  ⏬ Dataset download

Put dataset into data file:

- [Norman](https://figshare.com/articles/dataset/Norman_et_al_2019_Science_labeled_Perturb-seq_data/24688110)
- [Combosciplex subset of sciplex v3](https://figshare.com/articles/dataset/combosciplex/25062230?file=44229635)
### Alternative Data Access

We also provide the datasets via [Google Drive](https://drive.google.com/drive/folders/1cNpYAt9jVWZN82miNZtkP10YeSo7hufL?usp=sharing). This folder contains:
- The **Norman** dataset and its corresponding data splits.
- The **ComboSciPlex** dataset.

### Pretrained checkpoints

We also provide pretrained checkpoints via [Google Drive](https://drive.google.com/file/d/1ObRTXCt5_H3TIC54A6nOCKycltq88BwX/view?usp=drive_link), including:
- **Norman** checkpoints for two settings.
- **ComboSciPlex** checkpoints.

Example directory layout after download (relative to repo root):
```
scDFM/
├─ data/
│  ├─ norman.h5ad
│  └─ combosciplex.h5ad
├─ src/
│  └─ ...
└─ run.sh
```

## 📥 Training

An example on additive task.
```bash
bash run.sh
```

## 🫡 Citation

If you find our work and/or our code useful, please cite us via:

```bibtex
@inproceedings{yu2026scdfm,
  title={sc{DFM}: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction},
  author={Chenglei Yu and Chuanrui Wang and Bangyan Liao and Tailin Wu},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=QSGanMEcUV}
}
```

## 📚 Related Resources

- AI for Scientific Simulation and Discovery Lab: https://github.com/AI4Science-WestlakeU

scDFM

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