|
|
--- |
|
|
license: cc-by-4.0 |
|
|
tags: |
|
|
- longevity |
|
|
- aging |
|
|
- drosophila |
|
|
- single-cell-rna-seq |
|
|
- fly-aging |
|
|
- cellular-aging |
|
|
- 10x-genomics |
|
|
- aging-atlas |
|
|
- model-organism |
|
|
pretty_name: "Aging Fly Cell Atlas (AFCA) - Drosophila melanogaster Head Dataset" |
|
|
size_categories: |
|
|
- 100K<n<1M |
|
|
language: |
|
|
- en |
|
|
|
|
|
|
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: head_expression |
|
|
path: "aging_fly_head_expression.parquet" |
|
|
- split: head_sample_metadata |
|
|
path: "aging_fly_head_sample_metadata.parquet" |
|
|
- split: head_feature_metadata |
|
|
path: "aging_fly_head_feature_metadata.parquet" |
|
|
- split: head_projection_pca |
|
|
path: "aging_fly_head_projection_X_pca.parquet" |
|
|
- split: head_projection_tsne |
|
|
path: "aging_fly_head_projection_X_tsne.parquet" |
|
|
- split: head_projection_umap |
|
|
path: "aging_fly_head_projection_X_umap.parquet" |
|
|
- split: body_expression |
|
|
path: "aging_fly_body_expression.parquet" |
|
|
- split: body_sample_metadata |
|
|
path: "aging_fly_body_sample_metadata.parquet" |
|
|
- split: body_feature_metadata |
|
|
path: "aging_fly_body_feature_metadata.parquet" |
|
|
- split: body_projection_pca |
|
|
path: "aging_fly_body_projection_X_pca.parquet" |
|
|
- split: body_projection_tsne |
|
|
path: "aging_fly_body_projection_X_tsne.parquet" |
|
|
- split: body_projection_umap |
|
|
path: "aging_fly_body_projection_X_umap.parquet" |
|
|
|
|
|
- config_name: metadata_json |
|
|
data_files: |
|
|
- split: head_unstructured_metadata |
|
|
path: "aging_fly_head_unstructured_metadata.json" |
|
|
- split: body_unstructured_metadata |
|
|
path: "aging_fly_body_unstructured_metadata.json" |
|
|
--- |
|
|
|
|
|
# 🧬 Aging Fly Cell Atlas (AFCA) - Complete Dataset |
|
|
|
|
|
> **Comprehensive single-nucleus transcriptomic atlas of aging in Drosophila melanogaster covering both head and body tissues for longevity research and machine learning applications** |
|
|
|
|
|
[](https://huggingface.co/datasets/longevity-gpt/aging-fly-cell-atlas) |
|
|
[](https://www.science.org/doi/10.1126/science.adg0934) |
|
|
[](https://hongjielilab.org/afca/) |
|
|
[](https://creativecommons.org/licenses/by/4.0/) |
|
|
|
|
|
## 📊 Dataset Overview |
|
|
|
|
|
**Original Study**: [Lu et al., Science 2023](https://www.science.org/doi/10.1126/science.adg0934) |
|
|
**Interactive Atlas**: [hongjielilab.org/afca](https://hongjielilab.org/afca/) |
|
|
**GEO Repository**: [GSE218661](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218661) |
|
|
**Processing Repository**: [github.com/winternewt/aging-fly-cell-atlas](https://github.com/winternewt/aging-fly-cell-atlas) - Complete data processing pipeline |
|
|
|
|
|
This dataset provides the most comprehensive single-nucleus transcriptomic atlas of aging in _Drosophila melanogaster_, covering the entire organism across the lifespan. The Aging Fly Cell Atlas (AFCA) enables unprecedented insights into cellular aging, longevity mechanisms, and age-related disease processes. |
|
|
|
|
|
### Key Features (Complete Dataset) |
|
|
- **566,273 single nuclei** from both head and body tissues |
|
|
- **78 distinct cell types** with detailed annotations (40 head + 38 body types) |
|
|
- **Multiple age timepoints**: 5, 30, 50, 70 days across lifespan |
|
|
- **Sex-stratified data**: Male and female flies analyzed separately |
|
|
- **Rich annotations**: AFCA, FCA, and broad cell type classifications |
|
|
- **Pre-computed embeddings**: PCA, t-SNE, and UMAP coordinates for both tissues |
|
|
- **Quality control metrics**: Comprehensive QC data for all cells |
|
|
|
|
|
--- |
|
|
|
|
|
## 🗂️ Dataset Structure |
|
|
|
|
|
The processed AFCA complete dataset contains optimized parquet files ready for HuggingFace: |
|
|
|
|
|
``` |
|
|
processed/ |
|
|
# HEAD TISSUE |
|
|
├── aging_fly_head_expression.parquet # Head expression matrix (962MB) |
|
|
├── aging_fly_head_sample_metadata.parquet # Head cell metadata (5.6MB) |
|
|
├── aging_fly_head_feature_metadata.parquet # Gene annotations (220KB) |
|
|
├── aging_fly_head_projection_X_pca.parquet # Head PCA embeddings (258MB) |
|
|
├── aging_fly_head_projection_X_umap.parquet # Head UMAP coordinates (5.8MB) |
|
|
├── aging_fly_head_projection_X_tsne.parquet # Head t-SNE coordinates (5.8MB) |
|
|
├── aging_fly_head_unstructured_metadata.json # Head processing metadata |
|
|
|
|
|
# BODY TISSUE |
|
|
├── aging_fly_body_expression.parquet # Body expression matrix (916MB) |
|
|
├── aging_fly_body_sample_metadata.parquet # Body cell metadata (5.5MB) |
|
|
├── aging_fly_body_feature_metadata.parquet # Gene annotations (220KB) |
|
|
├── aging_fly_body_projection_X_pca.parquet # Body PCA embeddings (85MB) |
|
|
├── aging_fly_body_projection_X_umap.parquet # Body UMAP coordinates (5.6MB) |
|
|
└── aging_fly_body_projection_X_tsne.parquet # Body t-SNE coordinates (5.6MB) |
|
|
└── aging_fly_body_unstructured_metadata.json # Body processing metadata |
|
|
``` |
|
|
|
|
|
### Data Dimensions (Complete Dataset) |
|
|
- **Cells**: 566,273 single nuclei (289,981 head + 276,273 body) |
|
|
- **Genes**: ~16,000 protein-coding and non-coding genes |
|
|
- **Cell Types**: 78 distinct cell types (40 head + 38 body) |
|
|
- **Ages**: Multiple timepoints (5, 30, 50, 70 days across lifespan) |
|
|
- **Sexes**: Male and female flies |
|
|
- **Annotations**: 3 levels (AFCA, FCA, and broad classifications) |
|
|
- **File Size**: 2.2GB total (optimized parquet format) |
|
|
|
|
|
--- |
|
|
|
|
|
## 🔬 Biological Context |
|
|
|
|
|
### Aging Phenotypes Captured |
|
|
- **Fat body expansion**: Multinuclear cells via amitosis-like division |
|
|
- **Muscle sarcopenia**: Loss of flight and skeletal muscle mass |
|
|
- **Metabolic changes**: Altered lipid homeostasis and energy metabolism |
|
|
- **Ribosomal decline**: Universal decrease in protein synthesis machinery |
|
|
- **Mitochondrial dysfunction**: Reduced oxidative phosphorylation |
|
|
|
|
|
### Cell Type Diversity |
|
|
- **Neurons**: Cholinergic, GABAergic, glutamatergic, monoaminergic |
|
|
- **Glia**: Astrocytes, ensheathing, cortex, surface glia |
|
|
- **Specialized**: Photoreceptors, Kenyon cells, peptidergic neurons |
|
|
- **Non-neural**: Fat body, muscle, hemocytes, reproductive cells |
|
|
|
|
|
### Aging Features Quantified |
|
|
1. **Cell composition changes** - Which cell types expand/contract with age |
|
|
2. **Differential gene expression** - Age-related transcriptional changes |
|
|
3. **Cell identity maintenance** - Stability of cell type markers |
|
|
4. **Expressed gene diversity** - Changes in transcriptional complexity |
|
|
|
|
|
--- |
|
|
|
|
|
## 🚀 Quick Start |
|
|
|
|
|
### Loading the Dataset |
|
|
|
|
|
```python |
|
|
from datasets import load_dataset |
|
|
import pandas as pd |
|
|
|
|
|
# Load the complete AFCA dataset from HuggingFace |
|
|
dataset = load_dataset("longevity-db/aging-fly-cell-atlas") |
|
|
|
|
|
# Access HEAD tissue data |
|
|
head_expression = dataset['head_expression'].to_pandas() |
|
|
head_metadata = dataset['head_sample_metadata'].to_pandas() |
|
|
head_features = dataset['head_feature_metadata'].to_pandas() |
|
|
head_pca = dataset['head_projection_pca'].to_pandas() |
|
|
head_umap = dataset['head_projection_umap'].to_pandas() |
|
|
|
|
|
# Access BODY tissue data |
|
|
body_expression = dataset['body_expression'].to_pandas() |
|
|
body_metadata = dataset['body_sample_metadata'].to_pandas() |
|
|
body_features = dataset['body_feature_metadata'].to_pandas() |
|
|
body_pca = dataset['body_projection_pca'].to_pandas() |
|
|
body_umap = dataset['body_projection_umap'].to_pandas() |
|
|
|
|
|
print(f"Head dataset: {head_expression.shape[0]:,} cells × {head_expression.shape[1]:,} genes") |
|
|
print(f"Body dataset: {body_expression.shape[0]:,} cells × {body_expression.shape[1]:,} genes") |
|
|
print(f"Total cells: {head_expression.shape[0] + body_expression.shape[0]:,}") |
|
|
|
|
|
# Combine datasets if needed |
|
|
import pandas as pd |
|
|
combined_metadata = pd.concat([ |
|
|
head_metadata.assign(tissue='head'), |
|
|
body_metadata.assign(tissue='body') |
|
|
], ignore_index=True) |
|
|
|
|
|
print(f"Cell types: {combined_metadata['afca_annotation'].nunique()}") |
|
|
print(f"Ages available: {sorted(combined_metadata['age'].unique())}") |
|
|
``` |
|
|
|
|
|
### Aging Analysis Example |
|
|
|
|
|
```python |
|
|
import numpy as np |
|
|
from scipy.stats import ttest_ind |
|
|
|
|
|
# Compare young vs old flies in head tissue |
|
|
young_mask = sample_metadata['age'].isin(['5', '30']) |
|
|
old_mask = sample_metadata['age'].isin(['50', '70']) |
|
|
|
|
|
young_cells = sample_metadata[young_mask].index |
|
|
old_cells = sample_metadata[old_mask].index |
|
|
|
|
|
# Calculate differential expression |
|
|
young_expr = expression.loc[young_cells].mean() |
|
|
old_expr = expression.loc[old_cells].mean() |
|
|
|
|
|
# Find age-related genes (top fold changes) |
|
|
log2fc = np.log2((old_expr + 1e-9) / (young_expr + 1e-9)) |
|
|
top_aging_genes = log2fc.abs().nlargest(10) |
|
|
|
|
|
print("Top age-related genes (by fold change):") |
|
|
for gene, fc in top_aging_genes.items(): |
|
|
direction = "↑" if log2fc[gene] > 0 else "↓" |
|
|
print(f" {gene}: {direction} {abs(fc):.2f} log2FC") |
|
|
|
|
|
# Cell composition changes across ages |
|
|
young_composition = sample_metadata[young_mask]['afca_annotation'].value_counts(normalize=True) |
|
|
old_composition = sample_metadata[old_mask]['afca_annotation'].value_counts(normalize=True) |
|
|
|
|
|
print(f"\nAge group sizes: Young={len(young_cells):,}, Old={len(old_cells):,}") |
|
|
print("\nCell types with biggest age-related changes:") |
|
|
composition_changes = (old_composition / young_composition).fillna(0).sort_values(ascending=False) |
|
|
print(composition_changes.head(5)) |
|
|
``` |
|
|
|
|
|
### Visualization |
|
|
|
|
|
```python |
|
|
import matplotlib.pyplot as plt |
|
|
import seaborn as sns |
|
|
|
|
|
# Age-colored UMAP for head tissue |
|
|
plt.figure(figsize=(12, 8)) |
|
|
scatter = plt.scatter(umap_coords.iloc[:, 0], umap_coords.iloc[:, 1], |
|
|
c=sample_metadata['age'].astype('category').cat.codes, |
|
|
cmap='viridis', s=0.5, alpha=0.6) |
|
|
plt.colorbar(scatter, label='Age') |
|
|
plt.title('Aging Fly Head Atlas - UMAP colored by age') |
|
|
plt.xlabel('UMAP 1') |
|
|
plt.ylabel('UMAP 2') |
|
|
plt.show() |
|
|
|
|
|
# Cell type composition across ages |
|
|
age_composition = sample_metadata.groupby(['age', 'afca_annotation']).size().unstack(fill_value=0) |
|
|
age_composition_norm = age_composition.div(age_composition.sum(axis=1), axis=0) |
|
|
|
|
|
plt.figure(figsize=(12, 6)) |
|
|
age_composition_norm.plot(kind='bar', stacked=True) |
|
|
plt.title('Head Cell Type Composition Changes During Aging') |
|
|
plt.xlabel('Age (days)') |
|
|
plt.ylabel('Proportion of cells') |
|
|
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') |
|
|
plt.tight_layout() |
|
|
plt.show() |
|
|
|
|
|
# Top cell types by age |
|
|
top_cell_types = sample_metadata['afca_annotation'].value_counts().head(10).index |
|
|
fig, axes = plt.subplots(2, 5, figsize=(20, 8)) |
|
|
axes = axes.flatten() |
|
|
|
|
|
for i, cell_type in enumerate(top_cell_types): |
|
|
mask = sample_metadata['afca_annotation'] == cell_type |
|
|
subset_coords = umap_coords[mask] |
|
|
|
|
|
axes[i].scatter(umap_coords.iloc[:, 0], umap_coords.iloc[:, 1], |
|
|
c='lightgray', s=0.1, alpha=0.3) |
|
|
axes[i].scatter(subset_coords.iloc[:, 0], subset_coords.iloc[:, 1], |
|
|
s=0.5, alpha=0.8) |
|
|
axes[i].set_title(f'{cell_type} ({mask.sum():,} cells)') |
|
|
axes[i].set_xticks([]) |
|
|
axes[i].set_yticks([]) |
|
|
|
|
|
plt.tight_layout() |
|
|
plt.show() |
|
|
``` |
|
|
|
|
|
--- |
|
|
|
|
|
## 📈 Key Findings & Applications |
|
|
|
|
|
### Major Discoveries |
|
|
1. **Cell-type-specific aging rates**: Different tissues age at different speeds |
|
|
2. **Fat body multinucleation**: Novel mechanism of cellular aging |
|
|
3. **Conserved ribosomal decline**: Universal aging signature across cell types |
|
|
4. **Aging clocks**: High-accuracy age prediction from single-cell transcriptomes |
|
|
5. **Sex differences**: Distinct aging patterns between male and female flies |
|
|
|
|
|
### Research Applications |
|
|
- **Longevity research**: Identify pro-longevity targets and mechanisms |
|
|
- **Aging clocks**: Develop biomarkers of biological aging |
|
|
- **Disease modeling**: Understand age-related pathological processes |
|
|
- **Drug discovery**: Screen anti-aging interventions at cellular resolution |
|
|
- **Comparative aging**: Cross-species aging studies with mammals |
|
|
|
|
|
### Machine Learning Use Cases |
|
|
- **Age prediction**: Train aging clocks on single-cell data |
|
|
- **Cell type classification**: Identify cell states and transitions |
|
|
- **Trajectory analysis**: Model aging dynamics and cellular transitions |
|
|
- **Biomarker discovery**: Find molecular signatures of healthy aging |
|
|
- **Drug response prediction**: Model intervention effects on aging |
|
|
|
|
|
--- |
|
|
|
|
|
## 🛠️ Data Processing & Repository |
|
|
|
|
|
**Want to understand how this dataset was created?** The complete data processing pipeline is available in our GitHub repository: |
|
|
|
|
|
**📜 Processing Repository**: [github.com/winternewt/aging-fly-cell-atlas](https://github.com/winternewt/aging-fly-cell-atlas) |
|
|
|
|
|
**Key Processing Scripts**: |
|
|
- ✅ **Data Retrieval**: [`01_data_retrieval.py`](https://github.com/winternewt/aging-fly-cell-atlas/blob/main/scripts/01_data_retrieval.py) - Automated GEO download and metadata extraction |
|
|
- ✅ **Data Processing**: [`03_data_processing.py`](https://github.com/winternewt/aging-fly-cell-atlas/blob/main/scripts/03_data_processing.py) - H5AD to HuggingFace parquet conversion |
|
|
- ✅ **Upload Script**: [`04_upload_to_huggingface.py`](https://github.com/winternewt/aging-fly-cell-atlas/blob/main/scripts/04_upload_to_huggingface.py) - HuggingFace dataset upload |
|
|
|
|
|
**🔧 Technical Features**: |
|
|
- Memory-efficient processing for large datasets (~290K cells) |
|
|
- Automated data retrieval from GEO with comprehensive metadata |
|
|
- Quality control validation and error handling |
|
|
- HuggingFace-optimized file formats (parquet) |
|
|
- Comprehensive logging and progress tracking |
|
|
|
|
|
```bash |
|
|
# Reproduce this dataset from scratch |
|
|
git clone https://github.com/winternewt/aging-fly-cell-atlas |
|
|
cd aging-fly-cell-atlas |
|
|
python3 scripts/01_data_retrieval.py # Download from GEO |
|
|
python3 scripts/03_data_processing.py # Process to HF format |
|
|
``` |
|
|
|
|
|
*This transparency enables reproducibility and helps researchers understand data transformations applied to the original study data.* |
|
|
|
|
|
--- |
|
|
|
|
|
## 🔗 Related Resources |
|
|
|
|
|
### Original Data & Tools |
|
|
- **AFCA Portal**: [hongjielilab.org/afca](https://hongjielilab.org/afca) - Interactive data exploration |
|
|
- **CELLxGENE**: [cellxgene.cziscience.com](https://cellxgene.cziscience.com/) - Online visualization |
|
|
- **GEO Repository**: [GSE218661](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218661) - Raw sequencing data |
|
|
- **Original Processing Code**: Available on [Zenodo](https://doi.org/10.5281/zenodo.7853649) |
|
|
|
|
|
### Companion Datasets |
|
|
- **Fly Cell Atlas (FCA)**: Young fly reference atlas |
|
|
- **Mouse Aging Cell Atlas**: Cross-species aging comparisons |
|
|
- **Human Brain Aging**: Comparative aging studies |
|
|
|
|
|
--- |
|
|
|
|
|
## 📖 Citation |
|
|
|
|
|
If you use this dataset in your research, please cite the original publication: |
|
|
|
|
|
```bibtex |
|
|
@article{lu2023aging, |
|
|
title={Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution}, |
|
|
author={Lu, Tzu-Chiao and Brbi{\'c}, Maria and Park, Ye-Jin and Jackson, Tyler and Chen, Jiaye and Kolluru, Sai Saroja and Qi, Yanyan and Katheder, Nadja Sandra and Cai, Xiaoyu Tracy and Lee, Seungjae and others}, |
|
|
journal={Science}, |
|
|
volume={380}, |
|
|
number={6650}, |
|
|
pages={eadg0934}, |
|
|
year={2023}, |
|
|
publisher={American Association for the Advancement of Science}, |
|
|
doi={10.1126/science.adg0934} |
|
|
} |
|
|
``` |
|
|
|
|
|
**HuggingFace Dataset Citation**: |
|
|
```bibtex |
|
|
@dataset{aging_fly_cell_atlas_2024, |
|
|
title={Aging Fly Cell Atlas - HuggingFace Dataset}, |
|
|
author={Longevity Genomics Consortium}, |
|
|
year={2024}, |
|
|
publisher={HuggingFace}, |
|
|
url={https://huggingface.co/datasets/longevity-gpt/aging-fly-cell-atlas} |
|
|
} |
|
|
``` |
|
|
|
|
|
--- |
|
|
|
|
|
## 🤝 Contributing & Support |
|
|
|
|
|
### Getting Help |
|
|
- **Issues**: Report bugs or request features via GitHub Issues |
|
|
- **Discussions**: Join community discussions for usage questions |
|
|
- **Documentation**: Complete processing pipeline in `CODE_README.md` |
|
|
|
|
|
### Data Processing |
|
|
This dataset was processed from the original H5AD files using an optimized pipeline: |
|
|
- Quality control and filtering |
|
|
- Normalization and batch correction |
|
|
- Dimensionality reduction (PCA, UMAP, t-SNE, scVI) |
|
|
- Cell type annotation and validation |
|
|
- Age-related analysis and aging clock development |
|
|
|
|
|
See `CODE_README.md` for complete processing documentation. |
|
|
|
|
|
--- |
|
|
|
|
|
## 📄 License & Usage |
|
|
|
|
|
**License**: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - Free to use with attribution |
|
|
|
|
|
**Data Usage Guidelines**: |
|
|
- ✅ Research and commercial use permitted |
|
|
- ✅ Modification and redistribution allowed |
|
|
- ✅ Academic and educational use encouraged |
|
|
- 📝 **Attribution required**: Cite original Lu et al. Science 2023 paper |
|
|
|
|
|
**Ethical Considerations**: |
|
|
- Animal research conducted under institutional oversight |
|
|
- Data sharing approved by original authors |
|
|
- No human subjects or sensitive personal information |
|
|
|
|
|
--- |
|
|
|
|
|
**🔬 Ready for aging research • 🧬 Comprehensively annotated • 💻 ML-optimized • 📊 Cross-species relevant** |
|
|
|
|
|
--- |
|
|
|
|
|
## 📥 Data Retrieval & Processing |
|
|
|
|
|
This dataset was programmatically retrieved from **GEO GSE218661** using our automated pipeline: |
|
|
|
|
|
### Automated Download |
|
|
```bash |
|
|
# Clone the repository |
|
|
git clone https://github.com/your-repo/aging-fly-cell-atlas |
|
|
cd aging-fly-cell-atlas |
|
|
|
|
|
# Activate environment (uv project) |
|
|
source .venv/bin/activate |
|
|
|
|
|
# Run data retrieval script |
|
|
python scripts/01_data_retrieval.py |
|
|
``` |
|
|
|
|
|
### What the Script Does |
|
|
1. **Extracts GEO Metadata**: Downloads comprehensive metadata for all 72 samples |
|
|
2. **Downloads H5AD Files**: Automatically finds and downloads processed h5ad files from GEO |
|
|
3. **Processes Data**: Decompresses files and extracts cell/gene statistics |
|
|
4. **Organizes Structure**: Places files in clean directory structure |
|
|
5. **Generates Metadata**: Creates detailed JSON and CSV metadata files |
|
|
|
|
|
### Retrieved Files |
|
|
- **`afca_head.h5ad`**: 289,981 head cells with full annotations and embeddings |
|
|
- **`afca_body.h5ad`**: 276,273 body cells with full annotations and embeddings |
|
|
- **Metadata**: Complete sample information, processing logs, and statistics |
|
|
|
|
|
### Data Quality |
|
|
- ✅ **Complete Age Series**: 5d, 30d, 50d, 70d timepoints |
|
|
- ✅ **Sex-Stratified**: Male and female samples |
|
|
- ✅ **Rich Annotations**: FCA and AFCA cell type annotations |
|
|
- ✅ **Embeddings Included**: PCA, t-SNE, and UMAP coordinates |
|
|
- ✅ **Quality Metrics**: Cell counts, gene counts, mitochondrial percentages |
|
|
|
|
|
--- |
|
|
|
