File size: 2,959 Bytes
7604f34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | # 🧬 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)
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## 📖 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.
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## 📂 Files Included
- `TMS_expression_sparse.parquet` – Chunked gene expression matrix (cells × genes)
- `TMS_metadata.parquet` – Metadata per cell (tissue, age, sex, cell type, etc.)
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## 🚀 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()
```
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## 💡 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.
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## 📚 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
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## 🙏 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.
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