---
viewer: true
license: cc0-1.0
configs:
- config_name: train-cells
  data_dir: "data/train/cells.lance"
- config_name: train-expression
  data_dir: "data/train/expression.lance"
- config_name: train-genes
  data_dir: "data/train/genes.lance"
- config_name: test-cells
  data_dir: "data/test/cells.lance"
- config_name: test-expression
  data_dir: "data/test/expression.lance"
- config_name: test-genes
  data_dir: "data/test/genes.lance"
language:
- en
tags:
- biology
- chemistry
- RNA
- single-cell
- lance
- slaf
pretty_name: Tahoe-100M
---
# Tahoe-100M Dataset (SLAF Format)

## Attribution

**This is a re-release of data originally generated by [Tahoe Therapeutics](https://huggingface.co/tahoebio).**

* **Original Dataset**: [tahoebio/Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M)
* **Original Format**: Parquet files
* **This Release**: Same data in SLAF (Sparse Lazy Array Format)
* **License**: CC0-1.0 (Creative Commons CC0 1.0 Universal - Public Domain)
* **Original Citation**:

```
@article{zhang2025tahoe,
  title={Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling},
  author={Zhang, Jesse and Ubas, Airol A and de Borja, Richard and Svensson, Valentine and Thomas, Nicole and Thakar, Neha and Lai, Ian and Winters, Aidan and Khan, Umair and Jones, Matthew G and others},
  journal={bioRxiv},
  pages={2025--02},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}
```

For detailed information about the dataset, methodology, and original publication, please refer to the [original dataset repository](https://huggingface.co/datasets/tahoebio/Tahoe-100M).

## Dataset Description

Tahoe-100M is a giga-scale single-cell perturbation atlas consisting of over 100 million transcriptomic profiles from 50 cancer cell lines exposed to 1,100 small-molecule perturbations. Generated using Vevo Therapeutics' Mosaic high-throughput platform, Tahoe-100M enables deep, context-aware exploration of gene function, cellular states, and drug responses at unprecedented scale and resolution. This release provides the same data in SLAF format for compatibility with SLAF tools.

## Usage

This dataset is in [SLAF (Sparse Lazy Array Format)](https://slaf-project.github.io/slaf/) format, which uses the [Lance](https://lance.org/) table format for storage. You can use it with the `slafdb` library (for SLAF format), or `pylance` library (for direct Lance access).

### Using SLAF (Recommended for SLAF Format)

```bash
pip install slafdb
```

```python
# Load train dataset
hf_path = 'hf://datasets/slaf-project/Tahoe-100M'
from slaf import SLAFArray
train_slaf = SLAFArray(f"{hf_path}/data/train")
train_slaf.query("SELECT * FROM cells LIMIT 10")

# Load test dataset
test_slaf = SLAFArray(f"{hf_path}/data/test")
test_slaf.query("SELECT * FROM cells LIMIT 10")
```

### Using Lance Directly

```bash
pip install pylance
```

```python
# Load train dataset
hf_path = 'hf://datasets/slaf-project/Tahoe-100M'
import lance
train_lance = lance.dataset(f"{hf_path}/data/train/cells.lance")
train_lance.sample(10)

# Load test dataset
test_lance = lance.dataset(f"{hf_path}/data/test/cells.lance")
test_lance.sample(10)
```