---
license: apache-2.0
language:
- en
tags:
- transcriptomics
- single-cell
---

---
license: apache-2.0
language:
- en
tags:
- transcriptomics
- single-cell
- lung
- IPF
- COPD
---

# GSE136831 Single Cell RNA-seq Dataset: IPF Cell Atlas

## Dataset Description

### Overview
This dataset, known as the IPF Cell Atlas, contains single-cell RNA sequencing data from whole lung dissociates of patients with Idiopathic Pulmonary Fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD), and healthy control lungs. It provides valuable insights into cellular heterogeneity in lung diseases.

### Original Source
- **GEO Accession**: [GSE136831](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136831)
- **BioProject**: PRJNA563828
- **SRA**: SRP395406

### Dataset Details
- **Original Release Date**: May 12, 2020
- **Last Update Date**: December 5, 2022
- **Hugging Face Release Date**: September 27, 2024
- **Version**: 1.0

## Content Description

This Hugging Face dataset contains:
1. Full dataset (approximately 2GB) in h5ad format
2. Smaller subset for quick analysis and testing in h5ad format

### File Format
- H5AD (Hierarchical Data Format version 5 with Annotated Data)

### Dataset Contents
- Single-cell gene expression data
- Cell type annotations
- Sample metadata (disease status: IPF, COPD, or control)

## Citation

If you use this dataset in your research, please cite the original authors:

1. Adams TS, Schupp JC, Poli S, Ayaub EA et al. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. *Sci Adv* 2020 Jul;6(28):eaba1983. PMID: 32832599

## Ethical Considerations

This dataset contains anonymized human tissue data. Users should adhere to ethical guidelines for human subjects research and treat the data with appropriate respect and consideration.

## Acknowledgements

We thank the original authors and the GEO repository for making this valuable dataset publicly available. This Hugging Face version aims to make the dataset more accessible to the machine learning and bioinformatics communities.