# PIVOT

Perturbation-Informed Vector-field Optimization for Transcriptomic state control.

PIVOT learns a perturbation-conditioned flow map over single-cell state embeddings and
uses its Jacobian for differentiable inverse design: given a control cell state and a
desired target state, it nominates gene-level interventions that move cells toward the
target. The same model also does ordinary forward response prediction.

## Layout

```
src/
  data/         loading + preprocessing of perturb-seq data, splits
  models/       perturbation encoder, flow map, the PIVOT module
  training/     training loop and losses
  evaluation/   inference, rewards, metrics, baselines
  experiments/  drivers for the result tables, ablations, figures
  utils/
scripts/        figure generation, extra ablations, GEARS comparison
experiments/    saved result json
```

## Setup

```bash
pip install -r requirements.txt
```

Data is not committed. Download and preprocess from the public sources first (Norman 2019
and Replogle 2022 are pulled from scPerturb):

```bash
python -m src.data.preprocess norman
python -m src.data.preprocess replogle_k562
```

This writes a PCA(50) embedding over 2000 highly variable genes plus the held-out splits to
`data/processed/<dataset>/`.

## Running things

```bash
# train one model
python -m src.training.train --dataset norman --split perturbation

# forward + nomination tables
python -m src.experiments.run_tables --dataset norman --tables forward_cell forward_perturbation

# ablations
python -m src.experiments.run_ablations --dataset norman

# figures
python scripts/figures.py
```

The GEARS head-to-head runs in its own conda env (older torch + pyg), since the package is
finicky about versions:

```bash
bash scripts/setup_gears_env.sh
conda run -n pivot_gears python scripts/gears_ranking.py
```

## Models

Every model behind the paper is checkpointed under `models/`, and `scripts/train_all.py`
retrains them from scratch with the same configs and seed, so the numbers reproduce.

- `models/norman/{cell,perturbation,combination}/` - Norman 2019 (CRISPRa K562), one per split
- `models/replogle_k562/{cell,perturbation,gene}/` - Replogle 2022 (CRISPRi K562), one per split
- `models/ablations/norman_perturbation/` - the ablation variants (loss components, perturbation
  representation, training-data fraction, control-matching strategy), each a separate folder

each folder has `model.pt` (a plain torch state dict), `config.json` (the training config),
and `train_info.json` (history + run info). loading needs the matching preprocessed dataset,
since the perturbation encoder vocabulary comes from the data:

```python
import json, torch
from src.data.perturb_data import load_dataset
from src.training.train import TrainConfig, make_model

cfg = TrainConfig(**json.load(open("models/norman/perturbation/config.json")))
data = load_dataset(cfg.dataset)
model = make_model(data, cfg, device="cpu")
model.load_state_dict(torch.load("models/norman/perturbation/model.pt", map_location="cpu"))
model.eval()
```

To retrain everything:

```bash
python scripts/train_all.py
```

## License

MIT, Bryan Cheng 2026. See `LICENSE`.