| # GSE167186 – Single-Nucleus RNA-Seq of Aged Human Skeletal Muscle | |
| **Organism**: *Homo sapiens* | |
| **Tissue**: Human skeletal muscle | |
| **Samples**: 23 individuals (young and aged) | |
| **Technique**: 10x Genomics single-nucleus RNA-seq | |
| **Data Type**: Processed sparse expression matrix and cell metadata | |
| --- | |
| ## 🧭 Description | |
| This dataset contains single-nucleus transcriptomes from human skeletal muscle samples spanning young and aged adult individuals. The goal was to understand the molecular changes in muscle cells associated with aging. | |
| Each sample is represented by a `.h5` file in the original dataset, which we have converted into a unified sparse expression matrix and metadata file for easy access and analysis. | |
| --- | |
| ## 📂 Files Included | |
| - `GSE167186_expression_sparse.parquet` | |
| → Gene expression matrix (sparse, cells × genes), saved using `scipy.sparse.save_npz` | |
| - `GSE167186_metadata.parquet` | |
| → Cell-level metadata, including sample ID and batch assignment | |
| --- | |
| ## 📥 How to Use | |
| ```python | |
| import pandas as pd | |
| from scipy import sparse | |
| # Load expression matrix | |
| X = sparse.load_npz("GSE167186_expression_sparse.parquet") | |
| # Load metadata | |
| meta = pd.read_parquet("GSE167186_metadata.parquet") | |
| ``` | |
| --- | |
| ## 💡 Use Cases | |
| - Investigating age-related changes in skeletal muscle at single-cell resolution | |
| - Benchmarking trajectory inference or cell clustering tools | |
| - Identifying differentially expressed genes between young and aged muscle | |
| - Studying cell-type-specific transcriptional signatures of human aging | |
| --- | |
| ## 🔗 Citation | |
| If you use this dataset, please cite: | |
| > Rubenstein et al. (2022). "Single-nucleus transcriptomics of human skeletal muscle reveals molecular signatures of aging." *Nature Aging*. DOI: [10.1038/s43587-022-00221-1](https://doi.org/10.1038/s43587-022-00221-1) | |
| --- | |
| ## 🙏 Acknowledgments | |
| Original dataset from GEO Accession [GSE167186](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167186). | |
| Data curated and converted by Iris Lee as part of the 2025 Longevity x AI Hackathon. ### 🧑💻 Team: MultiModalMillenials. Iris Lee (`@iris8090`) |