| # 🧬 sc-ImmuAging – Human PBMC Single-Cell Aging Clock Dataset | |
| This dataset includes curated feature selections from peripheral blood mononuclear cells (PBMCs) used to train aging clock models across five major immune cell types. It was derived from the **sc-ImmuAging** project to understand how aging affects the immune system at the single-cell level using machine learning models. | |
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| ## 📦 Dataset Contents | |
| - `sc-ImmuAging.parquet` — Long-format data containing gene features per immune cell type: | |
| - CD4 T cells | |
| - CD8 T cells | |
| - Monocytes | |
| - NK cells | |
| - B cells | |
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| ## 💡 Use Cases | |
| - 🧠 **Aging Clock Development**: Train regression models to predict biological age per cell type. | |
| - 🔬 **Immune System Aging Analysis**: Study gene-level contributions to age-related changes across immune subsets. | |
| - 🧬 **Biomarker Discovery**: Identify robust transcriptomic signatures of aging in blood-derived cells. | |
| - 🛠 **Feature Selection Benchmarking**: Compare machine learning models and feature selection strategies in scRNA-seq datasets. | |
| - 📊 **Multi-Omics Integration**: Align transcriptomic aging features with epigenetic clocks or proteomics. | |
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| ## 📖 Citation | |
| If you use this dataset, please cite the original study: | |
| **Dos Santos, Osorio et al. (2022).** | |
| "A single-cell transcriptomic atlas of the human immune system reveals age-related changes in PBMC composition and function." | |
| *Science Advances*, 8(45):eabq3784. | |
| https://doi.org/10.1126/sciadv.abq3784 | |
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| ## 🧬 Dataset Description | |
| This dataset was extracted from the **sc-ImmuAging** study that built predictive aging clocks using PBMC single-cell RNA-seq profiles. The features represent selected gene markers associated with aging across five immune cell types. Each list was curated for machine learning model input. | |
| **Original Data Source**: | |
| [GitHub Repository](https://github.com/dosorio/sc-ImmuAging) | |
| [Published Paper](https://www.science.org/doi/10.1126/sciadv.abq3784) | |
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| ## 🙏 Acknowledgments | |
| - Original authors of the sc-ImmuAging dataset and publication. | |
| - Curated and converted to `parquet` format by **Iris Lee** for ease of machine learning usage. ### 🧑💻 Team: MultiModalMillenials. Iris Lee (`@iris8090`) | |
| - Thanks to the open science community enabling downstream applications of single-cell data. |