Create README.md

README.md

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+# sc-ImmuAging – Human PBMC Single Cell Aging Clock Dataset
+
+This repository contains a summary of tables extracted from the supplementary materials of the publication:
+
+> **"A single-cell immune clock of human aging"**  
+> *Science Advances, 2022*  
+> DOI: [10.1126/sciadv.abn5631](https://doi.org/10.1126/sciadv.abn5631)
+
+The extracted tables are converted into a structured `.parquet` file for easier use in computational pipelines.
+
+---
+
+## 📦 Dataset Description
+
+| Table      | Description                                                             |
+|------------|-------------------------------------------------------------------------|
+| Table S1   | Summary of scRNA-seq datasets used in this study (public + in-house)   |
+| Table S2   | Aging scores and model performance across models and cell types        |
+| Table S3   | Gene-level feature importance for predictive aging models              |
+
+These tables provide high-level information to replicate or interpret the immune aging clock models developed using single-cell RNA-seq data from human PBMCs.
+
+---
+
+## 🔧 Usage Instructions
+
+### Load the Parquet File in Python
+
+```python
+import pandas as pd
+
+df = pd.read_parquet("sciadv_abn5631_summary.parquet")
+print(df)
+```
+
+---
+
+## 💡 Use Cases
+
+- Investigating immune cell aging patterns in human PBMCs
+- Benchmarking single-cell predictive aging models
+- Training or validating ML models using gene-level feature importance
+- Augmenting multi-omics longevity studies
+
+---
+
+## 📚 Citation
+
+If you use this dataset, please cite:
+
+> Ma, L., et al. (2022). A single-cell immune clock of human aging. *Science Advances*, 8(46), eabn5631.  
+> DOI: [10.1126/sciadv.abn5631](https://doi.org/10.1126/sciadv.abn5631)
+
+---
+
+## 🙏 Acknowledgments
+
+This dataset is derived from the supplementary materials of the original publication.  
+Data conversion and formatting by **Iris Lee** for use in longevity-related research and AI health hackathons.