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 ---
license: cc0-1.0
tags:
- biology
- single-cell
- RNA
- chemistry
size_categories:
- 100M<n<1B
configs:
- config_name: expression_data
data_files: data/train-*
default: true
- config_name: sample_metadata
data_files: metadata/sample_metadata.parquet
- config_name: gene_metadata
data_files: metadata/gene_metadata.parquet
- config_name: drug_metadata
data_files: metadata/drug_metadata.parquet
- config_name: cell_line_metadata
data_files: metadata/cell_line_metadata.parquet
- config_name: obs_metadata
data_files: metadata/obs_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.
**Preprint**: [Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling](https://www.biorxiv.org/content/10.1101/2025.02.20.639398v1)
<img src="https://pbs.twimg.com/media/Gkpp8RObkAM-fxe?format=jpg&name=4096x4096" width="1024" height="1024">
## Quickstart
```python
from datasets import load_dataset
# Load dataset in streaming mode
ds = load_dataset("tahoebio/Tahoe-100m", streaming=True, split="train")
# View the first record
next(ds.iter(1))
```
### Tutorials
Please refer to our tutorials for examples on using the data, accessing metadata tables and converting to/from the anndata format.
Please see the [Data Loading Tutorial](tutorials/loading_data.ipynb) for a walkthrough on using the data.
<table>
<thead>
<tr>
<th>Notebook</th>
<th>URL</th>
<th>Colab</th>
</tr>
</thead>
<tbody>
<tr>
<td>Loading the dataset from huggingface, accessing metadata, mapping to anndata</td>
<td>
<a href="https://huggingface.co/datasets/tahoebio/Tahoe-100M/blob/main/tutorials/loading_data.ipynb" target="_blank">
Link
</a>
</td>
<td>
<a href="https://colab.research.google.com/#fileId=https://huggingface.co/datasets/tahoebio/Tahoe-100M/blob/main/tutorials/loading_data.ipynb" target="_blank">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/>
</a>
</td>
</tr>
</tbody>
</table>
### Community Resources
Here are a links to few resources created by the community. We would love to feature additional tutorials from the community, if you have built something on top of
Tahoe-100M, please let us know and we would love to feature your work.
<table>
<thead>
<tr>
<th>Resource</th>
<th>Contributor</th>
<th>URL</th>
</tr>
</thead>
<tbody>
<tr>
<td>Analysis guide for Tahoe-100M using rapids-single-cell, scanpy and dask</td>
<td><a href="https://github.com/scverse" target="_blank">SCVERSE</a></td>
<td><a href="https://github.com/theislab/vevo_Tahoe_100m_analysis/tree/tahoe-DGX-fix" target="_blank">Link</a></td>
</tr>
<tr>
<td>Tutorial for accessing Tahoe-100M h5ad files hosted by the Arc Institute</td>
<td><a href="https://github.com/ArcInstitute" target="_blank">Arc Institute</a></td>
<td><a href="https://github.com/ArcInstitute/arc-virtual-cell-atlas/blob/main/tahoe-100M/tutorial-py.ipynb" target="_blank">Link</a></td>
</tr>
</tbody>
</table>
## Dataset Features
We provide multiple tables with the dataset including the main data (raw counts) in the `expression_data` table as well as
various metadata in the `gene_metadata`,`sample_metadata`,`drug_metadata`,`cell_line_metadata`,`obs_metadata` tables.
The main data can be downloaded as follows:
```python
from datasets import load_dataset
tahoe_100m_ds = load_dataset("tahoebio/Tahoe-100M", streaming=True, split="train")
```
Setting `stream=True` instantiates an `IterableDataset` and prevents needing to
download the full dataset first. See [tutorial](tutorials/loading_data.ipynb) for an end-to-end example.
The expression_data table has the following fields:
| **Field Name** | **Type** | **Description** |
|------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `genes` | `sequence<int64>` | Gene identifiers (integer token IDs) corresponding to each gene with non-zero expression in the cell. This sequence aligns with the `expressions` field. The gene_metadata table can be used to map the token_IDs to gene_symbols or ensembl_IDs. The first entry for each row is just a marker token and should be ignored (See [data-loading tutorial](tutorials/loading_data.ipynb)) |
| `expressions` | `sequence<float32>` | Raw count values for each gene, aligned with the `genes` field. The first entry just marks a CLS token and should be ignored when parsing. |
| `drug` | `string` | Name of the treatment. DMSO_TF marks vehicle controls, use DMSO_TF along with plate to get plate matched controls. |
| `sample` | `string` | Unique identifier for the sample from which the cell was derived. Can be used to merge information from the `sample_metadata` table. Distinguishes replicate treatments. |
| `BARCODE_SUB_LIB_ID`| `string` | Combination of barcode and sublibary identifiers. Unique for each cell in the dataset. Can be used as an index key when referencing to the `obs_metadata` table. |
| `cell_line_id` | `string` | Unique identifier for the cancer cell line from which the cell originated. We use Cellosaurus IDs were, but additional identifiers such as DepMap IDs are provided in the `cell_line_metadata` table. |
| `moa-fine` | `string` | Fine-grained mechanism of action (MOA) annotation for the drug, specifying the biological process or molecular target affected. Derived from MedChemExpress and curated with GPT-based annotations. |
| `canonical_smiles` | `string` | Canonical SMILES (Simplified Molecular Input Line Entry System) string representing the molecular structure of the perturbing compound. |
| `pubchem_cid` | `string` | PubChem Compound Identifier for the drug, allowing cross-referencing with public chemical databases. An empty string is used for DMSO controls. Please cast to int before querrying pubchem. |
| `plate` | `string` | Identifier for the 96-well plate (1–14) in which the mixed-cell spheroid was seeded and treated. |
## Additional metadata
### Gene Metadata
```python
gene_metadata = load_dataset("taheobio/Tahoe-100M","gene_metadata", split="train")
```
| Column Name | Description |
|---------------|-------------------------------------------------------------------------------------------------------------|
| `gene_symbol` | The HGNC-approved gene symbol corresponding to each gene (e.g., *TP53*, *BRCA1*). |
| `ensembl_id` | The Ensembl gene identifier (e.g., *ENSG00000000003*) based on Ensembl release 109 and genome build 38. |
| `token_id` | An integer token ID used to represent each gene. This is the ID used in the `genes` field in the main data. |
### Sample Metadata
```python
sample_metadata = load_dataset("tahoebio/Tahoe-100M","sample_metadata", split="train")
```
The sample_metadata has additional information for aggregate quality metrics for the sample as well as the concentration.
| Column Name | Description |
|------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `sample` | Unique identifier for the sample from which the cell was derived. Unique key for this table. |
| `plate` | Identifier (1–14) for the 96-well plate for the sample |
| `mean_gene_count` | Average number of unique genes detected per cell for the given sample. |
| `mean_tscp_count` | Average number of transcripts (UMIs) detected per cell in the sample. |
| `mean_mread_count` | Average number of reads per cell. |
| `mean_pcnt_mito` | Mean percentage of total reads that map to mitochondrial genes, across cells in the sample. |
| `drug` | Name of the treatment used to perturb the cells in the sample. |
| `drugname_drugconc` | String combining the compound name, concentration and concentration unit (e.g., `[('8-Hydroxyquinoline',0.05,'uM')]`), used to uniquely label each treatment condition. |
### Drug Metadata
```python
drug_metadata = load_dataset("tahoebio/Tahoe-100M","drug_metadata", split="train")
```
The drug_metadata has additional information about each treatment.
| Column Name | Description |
|------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `drug` | Name of the treatment used to perturb the cells in the sample. Unique key for this table |
| `targets` | List of gene symbols representing the known molecular targets of the compound. Targets were proposed by GPT-4o based on compound names and then validated against MedChemExpress information. |
| `moa-broad` | Broad classification of the compound’s mechanism of action (MOA), typically categorized as "inhibitor/antagonist," "activator/agonist," or "unclear." GPT-4o inferred this using compound target data and curated descriptions from MedChemExpress. |
| `moa-fine` | Specific functional annotation of the compound's MOA (e.g., "Proteasome inhibitor" or "MEK inhibitor"). These fine-grained labels were selected from a curated list of 25 MOA categories and assigned by GPT-4o with validation against compound descriptions. |
| `human-approved` | Indicates whether the compound is approved for human use ("yes" or "no"). GPT-4o provided these labels using prior knowledge and validation from public sources such as clinicaltrials.gov. |
| `clinical-trials` | Indicates whether the compound has been evaluated in any registered clinical trials ("yes" or "no"). Determined using GPT-4o and corroborated using clinicaltrials.gov searches. |
| `gpt-notes-approval` | Contextual notes generated by GPT-4o summarizing the compound’s approval status, common clinical usage, or nuances such as formulation-specific approvals. |
| `canonical_smiles` | The compound's SMILES (Simplified Molecular Input Line Entry System) representation, capturing its molecular structure as a text string. |
| `pubchem_cid` | The PubChem Compound Identifier (CID), a unique numerical ID linking the compound to its entry in the PubChem database. |
### Cell Line Metadata
```python
cell_line_metadata = load_dataset("tahoebio/Tahoe-100M","cell_line_metadata", split="train")
```
The cell-line metadata table has additional information about the key driver mutations for each cell line.
| Column Name | Description |
|----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `cell_name` | Standard name of the cancer cell line (e.g., *A549*). |
| `Cell_ID_DepMap` | Unique identifier for the cell line in the DepMap project (e.g., *ACH-000681*) |
| `Cell_ID_Cellosaur` | Cellosaurus accession ID (e.g., *CVCL_0023*). This is the ID used in the main dataset. |
| `Organ` | Tissue or organ of origin for the cell line (e.g., *Lung*), used to interpret lineage-specific responses and biological context. |
| `Driver_Gene_Symbol` | HGNC-approved symbol of a known or putative driver gene with functional alterations in this cell line (e.g., *KRAS*, *CDKN2A*). We report a curated list of driver mutations per cell-line. |
| `Driver_VarZyg` | Zygosity of the driver variant (e.g., *Hom* for homozygous, *Het* for heterozygous) |
| `Driver_VarType` | Type of genetic alteration (e.g., *Missense*, *Frameshift*, *Stopgain*, *Deletion*) |
| `Driver_ProtEffect_or_CdnaEffect`| Specific protein or cDNA-level annotation of the mutation (e.g., *p.G12S*, *p.Q37*), providing precise information on the variant’s consequence. |
| `Driver_Mech_InferDM` | Inferred functional mechanism of the mutation (e.g., *LoF* for loss-of-function, *GoF* for gain-of-function) |
| `Driver_GeneType_DM` | Classification of the driver gene as an *Oncogene* or *Suppressor* |
## 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}
}
```