--- 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 **Comprehensive single-nucleus transcriptomic atlas of aging in Drosophila melanogaster covering both head and body tissues for longevity research and machine learning applications** [![Dataset](https://img.shields.io/badge/🤗%20Dataset-Aging%20Fly%20Cell%20Atlas-blue)](https://huggingface.co/datasets/longevity-gpt/aging-fly-cell-atlas) [![Paper](https://img.shields.io/badge/📖%20Paper-Science%202023-red)](https://www.science.org/doi/10.1126/science.adg0934) [![Data Portal](https://img.shields.io/badge/🌐%20AFCA%20Portal-Interactive-green)](https://hongjielilab.org/afca/) [![License](https://img.shields.io/badge/📄%20License-CC%20BY%204.0-yellow)](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 ---