---
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.

<img src="https://pbs.twimg.com/media/Gkpp8RObkAM-fxe?format=jpg&name=4096x4096" width="1024" height="1024">

## 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}
}
```