| # 🧬 Tabula Muris Senis – 10x Dataset (Mouse Aging Atlas) | |
| **Organism**: *Mus musculus* | |
| **Assay**: 10x Genomics Single Cell 3' v2 | |
| **Tissues**: 16 mouse tissues (e.g., heart, lung, kidney, liver) | |
| **Cells**: 245,000+ single cells | |
| **Age groups**: Spanning mouse lifespan (young to old) | |
| --- | |
| ## 📖 Dataset Description | |
| This dataset is a subset of the Tabula Muris Senis project, a collaborative effort to create a comprehensive single-cell transcriptomic atlas of aging in the mouse. The 10x portion of the data includes over 245,000 cells across 16 tissues profiled using droplet-based 10x Genomics technology. It provides a powerful resource for understanding how aging affects individual cell types across diverse tissues. | |
| --- | |
| ## 📂 Files Included | |
| - `TMS_expression_sparse.parquet` – Chunked gene expression matrix (cells × genes) | |
| - `TMS_metadata.parquet` – Metadata per cell (tissue, age, sex, cell type, etc.) | |
| --- | |
| ## 🚀 Usage Instructions | |
| ```python | |
| import pandas as pd | |
| # Load the expression and metadata files | |
| expression = pd.read_parquet("TMS_expression_sparse.parquet") | |
| metadata = pd.read_parquet("TMS_metadata.parquet") | |
| # Optionally merge for analysis | |
| df = expression.join(metadata) | |
| ``` | |
| Alternatively, load from Hugging Face: | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("longevity-db/tabula-muris-senis-10x") | |
| df = ds["train"].to_pandas() | |
| ``` | |
| --- | |
| ## 💡 Use Cases | |
| - **Transcriptomic Aging Analysis**: Discover how aging influences gene expression across cell types and tissues. | |
| - **Cross-Tissue Comparisons**: Study systemic versus tissue-specific aging trajectories. | |
| - **Biological Age Modeling**: Train machine learning models to predict biological age from transcriptomic signatures. | |
| - **Single-Cell Method Development**: Benchmark algorithms for clustering, integration, or trajectory inference on aging data. | |
| - **Sex-Specific Aging Research**: Explore differences in aging across male and female samples. | |
| - **Cross-Species Comparisons**: Integrate this dataset with human aging data to identify conserved mechanisms. | |
| --- | |
| ## 📚 Citation | |
| If you use this dataset, please cite the original publication: | |
| > **Tabula Muris Consortium** (2020). | |
| > *A single-cell transcriptomic atlas characterizes ageing tissues in the mouse.* | |
| > Nature, 583, 590–595. | |
| > https://doi.org/10.1038/s41586-020-2496-1 | |
| --- | |
| ## 🙏 Acknowledgments | |
| This dataset was produced by the [Tabula Muris Consortium](https://tabula-muris-senis.ds.czbiohub.org/), made possible by the **Chan Zuckerberg Biohub** and the **CZI Initiative**. | |
| The data was accessed via [cellxgene.cziscience.com](https://cellxgene.cziscience.com) and reformatted by **Iris Lee** for easier community use. ### 🧑💻 Team: MultiModalMillenials. Iris Lee (`@iris8090`) | |
| We acknowledge the developers of open-source tools such as `scanpy`, `anndata`, `pandas`, and `pyarrow` that made processing and sharing this dataset possible. | |
| --- |