README.md

metadata

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.

• Original Dataset: 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.

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) format, which uses the Lance 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)

pip install slafdb

# 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

pip install pylance

# 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)