Tahoe-x1-embeddings

# Tahoe-x1 Embeddings on Tahoe-100M Precomputed embeddings from the [Tahoe-x1](https://huggingface.co/tahoebio/Tahoe-x1) foundation model applied to the [Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M) dataset. This dataset provides high-dimensional representations of single-cell transcriptomic profiles from cancer cell lines under small-molecule perturbations. ## Overview This dataset contains cell embeddings generated using the **Tahoe-x1-3B** model, a 3 billion parameter perturbation-trained single-cell foundation model. The embeddings capture cellular states across: - **50 cancer cell lines** spanning multiple tissue types - **~1,100 small-molecule compounds** with diverse mechanisms of action - **100+ million single-cell profiles** from the original [Tahoe-100M dataset](https://huggingface.co/datasets/tahoebio/Tahoe-100M) These embeddings enable downstream applications such as drug response prediction, cell state classification, and perturbation effect analysis without requiring re-computation from raw expression data. For detailed information about the model architecture and training, see the [Tahoe-x1 model card](https://huggingface.co/tahoebio/Tahoe-x1). For information about the source data, see the [Tahoe-100M dataset card](https://huggingface.co/datasets/tahoebio/Tahoe-100M). ## Dataset Structure Each row in the dataset represents a single-cell profile with its corresponding embedding: | Column | Type | Description | |--------|------|-------------| | `drug` | `string` | Drug compound name (e.g., "8-Hydroxyquinoline") | | `sample` | `string` | Sample identifier from Tahoe-100M (e.g., "smp_1783") | | `cell_line` | `string` | Cellosaurus cell line identifier (e.g., "CVCL_1717", "CVCL_0480") | | `BARCODE_SUB_LIB_ID` | `string` | Unique barcode identifier for the sub-library (19 characters) | | `mosaicfm-3b-prod-cont-MFMv2` | `list[float]` | Cell embedding vector from Tahoe-x1-3B | **Note**: The embedding column name reflects the internal model version used during generation. Data files are stored in the `data/` directory in Parquet format for efficient streaming and loading. ## Quickstart ```python from datasets import load_dataset # Stream the dataset without downloading ds = load_dataset("tahoebio/Tahoe-x1-embeddings", streaming=True, split="train") # Get first example example = next(iter(ds)) print(example) ``` **Note**: If you encounter schema parsing errors, use this alternative: ```python from datasets import load_dataset # Load using parquet directly ds = load_dataset( "parquet", data_files="hf://datasets/tahoebio/Tahoe-x1-embeddings/data/*.parquet", streaming=True, split="train" ) ``` ## Source Information Embeddings generated using the [Tahoe-x1-3B](https://huggingface.co/tahoebio/Tahoe-x1) model on the [Tahoe-100M](https://huggingface.co/datasets/tahoebio/Tahoe-100M) dataset. ## Linking to Tahoe-100M Metadata To enrich these embeddings with additional metadata from Tahoe-100M: ```python from datasets import load_dataset # Load embeddings embeddings = load_dataset("tahoebio/Tahoe-x1-embeddings", split="train") # Load drug metadata drug_metadata = load_dataset("tahoebio/Tahoe-100M", "drug_metadata", split="train") # Load cell line metadata cell_line_metadata = load_dataset("tahoebio/Tahoe-100M", "cell_line_metadata", split="train") # Merge using pandas import pandas as pd df_emb = embeddings.to_pandas() df_drugs = drug_metadata.to_pandas() df_cells = cell_line_metadata.to_pandas() # Join on drug name and cell line df_enriched = df_emb.merge(df_drugs, on='drug').merge(df_cells, on='cell_line') print(f"Enriched dataset shape: {df_enriched.shape}") ``` ## License Apache 2.0 (inherited from Tahoe-x1 model) ## Resources - 🤗 [Tahoe-x1 Model Card](https://huggingface.co/tahoebio/Tahoe-x1) - 🤗 [Tahoe-100M Dataset Card](https://huggingface.co/datasets/tahoebio/Tahoe-100M) - 🚀 [Tahoe-x1 Interactive Demo](https://huggingface.co/spaces/tahoebio/Tahoe-x1) - 📧 Contact: [e-mail] ## Acknowledgments This dataset builds upon the foundational work of Tahoe Therapeutics and Vevo Therapeutics in creating large-scale single-cell perturbation atlases and state-of-the-art foundation models for cellular biology.