metadata

license: mit
tags:
  - biology
size_categories:
  - 10M<n<100M
configs:
  - config_name: expression_data
    data_files: data/train-*
  - config_name: sample_metadata
    data_files: metadata/sample_metadata.parquet
  - config_name: gene_vocabulary
    data_files: metadata/gene_vocabulary.json
  - config_name: drug_metadata
    data_files: metadata/drug_metadata.parquet
  - config_name: cell_line_metadata
    data_files: metadata/cell_line_metadata.parquet
dataset_info:
  features:
    - name: genes
      sequence: int64
    - name: expressions
      sequence: float32
    - name: drug
      dtype: string
    - name: sample
      dtype: string
    - name: BARCODE_SUB_LIB_ID
      dtype: string
    - name: cell_line_id
      dtype: string
    - name: moa-fine
      dtype: string
    - name: canonical_smiles
      dtype: string
    - name: pubchem_cid
      dtype: string
    - name: plate
      dtype: string
  splits:
    - name: train
      num_bytes: 1693653078843
      num_examples: 95624334
  download_size: 337644770670
  dataset_size: 1693653078843

Tahoe-100M

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 dataset is designed to power the development of next-generation AI models of cell biology, offering broad applications across systems biology, drug discovery, and precision medicine.

Dataset Description

Features

The dataset has the following fields:

Additional metadata

How to use

Citation

Please cite:

@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}
}