longevity-db/sciadv_abn5631_summary

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.

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📦 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.

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🔧 Usage Instructions

Load the Parquet File in Python

import pandas as pd

df = pd.read_parquet("sciadv_abn5631_summary.parquet")
print(df)

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💡 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

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📚 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

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🙏 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)