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
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
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
🙏 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. ### 🧑💻 Team: MultiModalMillenials. Iris Lee (@iris8090)