# 🧬 GSE120180 – Single-Cell Transcriptomics of Aging Human Skin

This dataset contains single-cell RNA-seq profiles from aging human skin, originally published as part of the GEO Series GSE120180. The dataset has been converted to `.parquet` format for faster I/O and compatibility with machine learning pipelines.

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## 📂 Dataset Overview

- **Original Source:** [GEO: GSE120180](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120180)
- **Species:** Homo sapiens  
- **Tissue:** Human skin  
- **Technique:** 10x Genomics scRNA-seq  
- **Format:** `.parquet` (converted from original `.txt.gz`)

Each `.parquet` file contains gene expression matrices with:
- **Rows:** Gene identifiers (ENSEMBL or gene symbols)
- **Columns:** Cell barcodes

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## 🔬 Use Cases

- Build or validate **skin-specific aging clocks**
- Study **age-related changes** in gene expression at single-cell resolution
- Explore **cell-type-specific aging signatures** in skin
- Benchmark **de-noising** or **imputation models** for sparse single-cell data
- Integrate with multi-tissue atlases or **multi-omics** aging datasets

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## 🛠️ Usage Instructions

```python
import pandas as pd

# Load one of the files
df = pd.read_parquet("GSM#####_expression.parquet")
print(df.shape)
df.head()
```

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## 📑 Citation

If you use this dataset, please cite:

> Solé-Boldo, L. et al. (2020). **Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming**. *Cell Stem Cell*, 27(3), 387–402.e7.  
> DOI: [10.1016/j.stem.2020.07.009](https://doi.org/10.1016/j.stem.2020.07.009)

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## 🙏 Acknowledgments

- Original data generated by **Solé-Boldo et al.** and hosted on **GEO** under accession [GSE120180](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120180)
- Converted and curated by **Iris Lee** for use in aging and longevity research. ### 🧑‍💻 Team: MultiModalMillenials. Iris Lee (`@iris8090`)


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## 🧠 Keywords

`single-cell`, `scRNA-seq`, `aging`, `skin`, `GSE120180`, `longevity`, `parquet`, `machine learning`, `biomarkers`