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Tahoe-100M

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metadata
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

Quickstart 

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.

Notebook URL Colab
Loading the dataset from huggingface, accessing metadata, mapping to anndata Link Open in Colab

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.

Resource Contributor URL
Analysis guide for Tahoe-100M using rapids-single-cell, scanpy and dask scverse Link

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:

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 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)
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 

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 

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 

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 

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}
}