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tahoebio/Tahoe-100M

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… See the full description on the dataset page: https://huggingface.co/datasets/tahoebio/Tahoe-100M.

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Dataset Card

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

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 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/loadingdata.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/loadingdata.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/vevoTahoe100manalysis/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 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 genemetadata table can be used to map the tokenIDs to genesymbols or ensemblIDs. The first entry for each row is just a marker token and should be ignored (See data-loading tutorial)
`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. DMSOTF marks vehicle controls, use DMSOTF 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}
}