---
license: cc-by-4.0
language:
- en
tags:
- biology
- single-cell
- perturbation
- transcriptomics
- drug-response
- foundation-model
- genomics
size_categories:
- 100M<n<1B
pretty_name: Perturb Sapiens
---

# Perturb Sapiens: A Human Whole-Organism Atlas of Perturbed Cells


## Dataset Description

**Perturb Sapiens** is an evolving database of AI-predicted single-cell perturbation responses, representing the **first human whole-organism atlas of perturbed cells**. 

Perturb Sapiens is generated using the post-trained **Stack model** ([Stack-Large-Aligned](https://huggingface.co/arcinstitute/Stack-Large-Aligned)), an in-context learning foundation model for single-cell biology.

**Data Sources:**
- **Prompt Data:** Parse/OpenProblems PBMC perturbation data
- **Query Data:** [Tabula Sapiens](https://tabula-sapiens.sf.czbiohub.org/) 

### Key Statistics (v1)

| Metric | Value |
|--------|-------|
| Total Cells | 103+ million |
| Human Tissues | 28 |
| Cell Classes | 40 |
| Drug Conditions | 111 (control: Dimethyl sulfoxide) |
| Cytokine Conditions | 90 (control: PBS) |



## Dataset Structure
```
PerturbSapiensv1/
├── drug/
│   ├── [drug_name].h5ad
│   └── Dimethyl sulfoxide.h5ad  # Control
└── cytokine/
    ├── [cytokine_name].h5ad
    └── PBS.h5ad                  # Control
```


## Quick Start
```python
import scanpy as sc

# Load a perturbation condition (e.g., ADSF cytokine)
adata = sc.read_h5ad('PerturbSapiensv1/cytokine/ADSF.h5ad')

# Preprocessing (aligned with post-training settings)
sc.experimental.pp.highly_variable_genes(adata, n_top_genes=4000)
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)

# Subset to high quality cells
adata = adata[adata.obs['gen_logit']<2.5].copy()

# Subset to highly variable genes
adata = adata[:, adata.var['highly_variable']].copy()

# Ready for downstream analysis!
```

## Intended Uses

- **Drug response prediction:** Explore how drugs affect gene expression across tissues
- **Cytokine signaling studies:** Understand tissue-specific responses to immune signals  
- **Hypothesis generation:** Identify potential therapeutic targets

## Limitations

- Predictions are generated by a foundation model and should be validated experimentally
- Potentially less precision in predicting effects of weak perturbations

## Citation

If you use Perturb Sapiens in your research, please cite:
- Dong et al., 2026: Stack: In-context modeling of single-cell biology. Preprint. [Paper link](https://www.biorxiv.org/content/10.64898/2026.01.09.698608v1)

## Links

- 🔗 **GitHub:** [ArcInstitute/stack](https://github.com/ArcInstitute/stack)
- 🤗 **Model:** [Stack-Large-Aligned](https://huggingface.co/arcinstitute/Stack-Large-Aligned)

## License

This dataset is released under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).

## Contact

For questions or issues, please open an issue on the [GitHub repository](https://github.com/ArcInstitute/stack/issues).