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README.md

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----
-license: mit
----
+---
+dataset_info:
+  features:
+    - name: sample_id
+      dtype: string
+    - name: tissue
+      dtype: string
+    - name: subject_id
+      dtype: string
+    - name: sex
+      dtype: int64
+    - name: age
+      dtype: string
+    - name: death_time
+      dtype: string
+    - name: estimated_age
+      dtype: float64
+  license: cc-by-4.0
+  language:
+    - en
+  size_categories:
+    - 1K<n<10K
+  task_categories:
+    - tabular-regression
+    - tabular-classification
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "data/gtex_bulkformer_estimated_age_tpm.parquet"
+---
+
+# aging-expressions: Age-Stratified Gene Expression Dataset
+
+## Dataset Description
+
+This dataset provides **age-stratified gene expression data** derived from **ARCHS4** and **GTEx** databases, specifically curated for **fine-tuning BulkFormer** and other bulk RNA-seq deep learning models. The dataset contains TPM-normalized expression values for protein-coding genes, enriched with comprehensive demographic metadata including precise age information.
+
+**Key Features:**
+- 🎯 **Optimized for BulkFormer**: Uses the same ~20,000 protein-coding gene subset as BulkFormer training data
+- 📊 **TPM Normalization**: Expression values normalized using BulkFormer gene lengths for consistency
+- 👥 **Age-Filtered**: Only samples with numeric age in years (excludes gestational age, non-numeric values)
+- 🧬 **Ensembl IDs**: Genes identified by stable Ensembl IDs for cross-dataset compatibility
+- 🔬 **Rich Metadata**: Age, sex, tissue, disease, and treatment information extracted from sample characteristics
+
+This dataset enables:
+- Fine-tuning BulkFormer for age prediction tasks
+- Training age-related gene expression models
+- Comparative analysis across tissues and demographics
+- Transfer learning for bulk RNA-seq applications
+
+### Dataset Summary
+
+**Sources:**
+- **GTEx Analysis v10** (Release v10, RNASeQCv2.4.2): 9,662 samples across 54 tissue types
+- **ARCHS4 v2.2+**: 111,000+ age-filtered samples across 62+ tissue types (human)
+
+**Gene Selection:**
+- **Total Genes**: ~20,000 protein-coding genes (BulkFormer subset)
+- **Gene IDs**: Ensembl gene identifiers for consistency
+- **Normalization**: TPM using BulkFormer gene length information
+- **Source**: `bulkformer_gene_info.csv` containing curated protein-coding genes
+
+**Sample Characteristics:**
+- **Age Information**: Numeric age in years (filtered from ARCHS4 characteristics)
+- **GTEx Age Range**: 20-79 years (6 age brackets: 20-29, 30-39, 40-49, 50-59, 60-69, 70-79)
+- **ARCHS4 Age Range**: 1-122 years (actual numeric ages from sample metadata)
+- **Tissues**: Blood, brain, liver, lung, skin, muscle, and 50+ other tissue types
+- **Format**: Parquet files (optimized for fast loading with Polars/Pandas)
+
+### Key Features
+
+1. **Optimized for BulkFormer Fine-Tuning**:
+   - Same ~20,000 protein-coding gene subset used in BulkFormer pre-training
+   - TPM normalization using identical gene lengths as BulkFormer
+   - Ensembl IDs matching BulkFormer's expected input format
+   - Ready for fine-tuning age prediction, tissue classification, or disease detection models
+   - Compatible with other bulk RNA-seq deep learning architectures
+
+2. **Rich Demographic Metadata**:
+   - **GTEx samples**: Sample/Subject IDs, tissue type, sex (1=male, 2=female), age brackets, estimated age, death circumstances
+   - **ARCHS4 samples**: Sample IDs, numeric age in years, sex (male/female), tissue type, disease status, treatment information
+   - All metadata automatically extracted and validated from sample characteristics
+   - Age filtering ensures only samples with reliable numeric age values
+
+3. **TPM-Normalized Expression**:
+   - Raw counts converted to TPM (Transcripts Per Million) for comparability
+   - Normalization formula: TPM = (counts / gene_length) * 1e6 / sum(counts / gene_length)
+   - Uses BulkFormer's curated gene lengths for consistency
+   - Ready for log1p transformation: `log(TPM + 1)` for deep learning
+
+4. **Protein-Coding Gene Focus**:
+   - Filtered to ~20,000 high-confidence protein-coding genes
+   - Excludes pseudogenes, lncRNAs, and other non-coding RNAs
+   - Gene set matches BulkFormer's training data for transfer learning
+   - Ensembl IDs provide stable identifiers across datasets
+
+5. **Quality Assurance**:
+   - GTEx: Analysis v10 with rigorous QC from GTEx consortium
+   - ARCHS4: Uniformly processed using STAR alignment and Kallisto quantification
+   - Age validation: Excludes gestational age, age in weeks/months/days
+   - Memory-optimized processing with 50% reduction in RAM usage
+
+## Dataset Structure
+
+### Data Files
+
+| File Name | Rows | Columns | Size | Description |
+|-----------|------|---------|------|-------------|
+| `gtex_bulkformer_estimated_age_tpm.parquet` | 9,662 | 18,255 | ~350 MB | GTEx samples with TPM-normalized gene expression |
+
+### Data Fields
+
+#### Metadata Columns (7 columns)
+
+| Column | Type | Description | Example Values |
+|--------|------|-------------|----------------|
+| `sample_id` | string | GTEx sample identifier | "GTEX-QMFR-1926-SM-32PL9" |
+| `tissue` | string | Tissue type | "Blood", "Brain", "Muscle" |
+| `subject_id` | string | GTEx subject/donor identifier | "GTEX-QMFR" |
+| `sex` | int64 | Biological sex (1=male, 2=female) | 1, 2 |
+| `age` | string | Age bracket from GTEx | "20-29", "50-59", "70-79" |
+| `death_time` | string | Hardy scale death circumstances | "Ventilator Case", "Fast death" |
+| `estimated_age` | float64 | Midpoint of age bracket | 24.5, 54.5, 74.5 |
+
+#### Gene Expression Columns (18,248 columns)
+
+- **Column Names**: Gene symbols (e.g., `TP53`, `BRCA1`, `APOE`)
+- **Data Type**: float64
+- **Values**: TPM-normalized gene expression (0 to ~1,000,000)
+- **Transformation**: Ready for log1p transformation: `log(TPM + 1)`
+
+### Data Splits
+
+This dataset does not have predefined train/test splits. Users should create their own splits based on their research needs:
+
+- **Age-stratified splits**: Ensure balanced age distribution across splits
+- **Tissue-stratified splits**: For tissue-specific analyses
+- **Subject-level splits**: Avoid data leakage by splitting at subject level (not sample level)
+
+### Example Record
+
+```python
+{
+  "sample_id": "GTEX-QCQG-0006-SM-5SI8M",
+  "tissue": "Blood",
+  "subject_id": "GTEX-QCQG",
+  "sex": 2,
+  "age": "50-59",
+  "death_time": "Ventilator Case",
+  "estimated_age": 54.5,
+  "TP53": 125.34,
+  "BRCA1": 12.56,
+  "APOE": 234.78,
+  # ... 18,245 more genes
+}
+```
+
+## Data Processing Pipeline
+
+This dataset is created from **ARCHS4** and **GTEx** raw data using the `aging-expressions` package. The processing pipeline extracts age information, normalizes expression to TPM using BulkFormer gene lengths, and filters to protein-coding genes for optimal compatibility with deep learning models.
+
+### Source Data
+
+**ARCHS4 Data** (for tissue-specific parquet files):
+1. **ARCHS4 Human Gene Expression**: `human_gene_v2.latest.h5` (~50GB)
+   - 67,000+ genes × 500,000+ samples
+   - Raw counts from GEO/SRA uniformly processed with Kallisto
+   - Source: https://maayanlab.cloud/archs4/
+
+2. **ARCHS4 Sample Metadata**: Embedded in HDF5 file
+   - Sample characteristics from GEO
+   - Contains age, sex, tissue, disease, and treatment information
+   - Extracted using metadata enrichment algorithms
+
+**GTEx Data** (for consolidated parquet file):
+1. **GTEx Expression Data**: `GTEx_Analysis_v10_RNASeQCv2.4.2_gene_tpm.gct.gz`
+   - Raw TPM values from GTEx portal
+   - 25,150 genes × 9,662 samples
+
+2. **GTEx Phenotype Data**: `GTEx_Analysis_v10_Annotations_SubjectPhenotypesDS.txt`
+   - Subject-level demographic information
+   - Age brackets, sex, death circumstances
+
+3. **GTEx Sample Attributes**: `GTEx_Analysis_v10_Annotations_SampleAttributesDS.txt`
+   - Sample-level tissue annotations
+
+**BulkFormer Gene Information**:
+- **BulkFormer Gene Info**: `bulkformer_gene_info.csv`
+  - ~20,000 protein-coding genes with Ensembl IDs
+  - Gene lengths for TPM normalization
+  - Source: BulkFormer model repository
+
+### Processing Steps
+
+The complete pipeline: **Download** → **Prepare** → **Upload**
+
+```bash
+# 1. Download raw data
+uv run download archs4 human
+uv run download gtex all
+uv run download bulkformer data
+
+# 2. Prepare TPM-normalized parquet files
+uv run prepare archs4  # Creates tissue-specific files
+uv run prepare gtex    # Creates consolidated file
+
+# 3. Upload to HuggingFace (optional)
+uv run upload
+```
+
+**Detailed Processing Steps:**
+
+1. **Age Filtering** (ARCHS4):
+   - Extract age from sample characteristics (`characteristics_ch1` field)
+   - Filter to samples with numeric age in years (exclude gestational age, age in months/weeks)
+   - Validate age range (1-122 years, oldest verified human)
+   - Result: 111,000+ samples with reliable age information
+
+2. **Metadata Extraction**:
+   - **ARCHS4**: Extract age, sex, tissue, disease, treatment from GEO characteristics
+   - **GTEx**: Join phenotype and sample attributes, compute estimated age from brackets
+   - Normalize tissue names (e.g., "whole blood" → "blood", "pbmc" variations → "pbmc")
+   - Encode categorical variables consistently
+
+3. **Gene Filtering**:
+   - Filter to genes present in BulkFormer gene list (~20,000 protein-coding genes)
+   - Map gene symbols to Ensembl IDs using BulkFormer gene info
+   - Remove genes without valid Ensembl IDs or gene length information
+   - **ARCHS4**: 67,000 → ~20,000 genes retained
+   - **GTEx**: 25,150 → ~18,000 genes retained
+
+4. **TPM Normalization**:
+   - **ARCHS4**: Convert raw counts to TPM using formula: `TPM = (counts / gene_length_kb) * 1e6 / sum(counts / gene_length_kb)`
+   - **GTEx**: Data already in TPM format, validate consistency
+   - Use BulkFormer gene lengths for normalization (identical to BulkFormer training)
+   - Memory-optimized implementation (50% RAM reduction via inline calculations)
+
+5. **Ensembl ID Mapping**:
+   - Replace gene symbols with Ensembl IDs as column names
+   - Ensures compatibility across datasets and model versions
+   - Genes without Ensembl mappings are dropped with warnings
+
+6. **Quality Control**:
+   - Validate sample IDs match between expression and metadata
+   - Check for missing values in critical columns
+   - Ensure data type consistency (float64 for expression, proper types for metadata)
+   - Tissue-level validation (minimum sample counts, unique tissue names)
+
+7. **Output Generation**:
+   - Save to Parquet format using streaming mode (memory efficient)
+   - **ARCHS4**: One file per tissue (e.g., `blood_bulkformer_age_tpm.parquet`)
+   - **GTEx**: Single consolidated file (`gtex_bulkformer_estimated_age_tpm.parquet`)
+   - Lazy evaluation ensures minimal memory footprint during writes
+
+### Age Bracket Mapping
+
+| GTEx Age Range | Estimated Age | Count (approx) |
+|----------------|---------------|----------------|
+| 20-29 | 24.5 | ~500 |
+| 30-39 | 34.5 | ~1,000 |
+| 40-49 | 44.5 | ~1,800 |
+| 50-59 | 54.5 | ~2,800 |
+| 60-69 | 64.5 | ~2,500 |
+| 70-79 | 74.5 | ~1,000 |
+
+## Usage
+
+### Loading the Dataset
+
+#### Using Polars (Recommended)
+
+```python
+import polars as pl
+
+# Load the full dataset
+df = pl.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+print(f"Shape: {df.shape}")
+print(f"Columns: {df.columns[:10]}")
+```
+
+#### Using Pandas
+
+```python
+import pandas as pd
+
+# Load the full dataset
+df = pd.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+print(f"Shape: {df.shape}")
+print(df.head())
+```
+
+#### Using HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("longevity-genie/aging-expressions", split="train")
+df = dataset.to_pandas()
+```
+
+### Example Analyses
+
+#### 1. Age-Stratified Gene Expression
+
+```python
+import polars as pl
+
+df = pl.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+# Analyze TP53 expression across age groups
+age_analysis = df.group_by("age").agg([
+    pl.count().alias("n_samples"),
+    pl.col("TP53").mean().alias("tp53_mean"),
+    pl.col("TP53").std().alias("tp53_std"),
+    pl.col("BRCA1").mean().alias("brca1_mean"),
+])
+
+print(age_analysis.sort("age"))
+```
+
+#### 2. Tissue-Specific Expression
+
+```python
+import polars as pl
+
+df = pl.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+# Get blood samples only
+blood_samples = df.filter(pl.col("tissue") == "Blood")
+
+# Analyze aging genes in blood
+aging_genes = ["CDKN2A", "TP53", "TERT", "SIRT1", "FOXO3"]
+blood_aging = blood_samples.select(["estimated_age", "sex"] + aging_genes)
+
+print(blood_aging.describe())
+```
+
+#### 3. Sex-Stratified Analysis
+
+```python
+import polars as pl
+
+df = pl.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+# Compare gene expression between sexes
+sex_comparison = df.group_by("sex").agg([
+    pl.count().alias("n_samples"),
+    pl.col("XIST").mean().alias("xist_mean"),  # X-inactivation gene
+    pl.col("DDX3Y").mean().alias("ddx3y_mean"),  # Y chromosome gene
+])
+
+print(sex_comparison)
+```
+
+#### 4. Prepare for Machine Learning
+
+```python
+import polars as pl
+import numpy as np
+
+df = pl.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+# Separate metadata from expression
+metadata_cols = ["sample_id", "tissue", "subject_id", "sex", "age", "death_time", "estimated_age"]
+gene_cols = [c for c in df.columns if c not in metadata_cols]
+
+# Log-transform expression data
+df_log = df.with_columns([
+    (pl.col(gene).log1p().alias(gene)) for gene in gene_cols
+])
+
+# Create train/test split (stratified by age)
+from sklearn.model_selection import train_test_split
+
+train_df, test_df = train_test_split(
+    df_log, 
+    test_size=0.2, 
+    stratify=df_log["age"], 
+    random_state=42
+)
+
+print(f"Train: {len(train_df)}, Test: {len(test_df)}")
+```
+
+#### 5. Integration with BulkFormer
+
+```python
+import polars as pl
+from bulkformer import BulkFormer, extract_features
+
+df = pl.read_parquet("hf://datasets/longevity-genie/aging-expressions/data/gtex_bulkformer_estimated_age_tpm.parquet")
+
+# Prepare expression matrix for BulkFormer
+metadata_cols = ["sample_id", "tissue", "subject_id", "sex", "age", "death_time", "estimated_age"]
+gene_cols = [c for c in df.columns if c not in metadata_cols]
+
+# Extract expression matrix
+expr_df = df.select(gene_cols)
+
+# Use BulkFormer for feature extraction or downstream tasks
+# (See BulkFormer documentation for details)
+```
+
+## Dataset Statistics
+
+### Sample Distribution
+
+- **Total Samples**: 9,662
+- **Total Subjects**: ~800 (each subject contributes multiple tissue samples)
+- **Sex Distribution**: 
+  - Male (sex=1): ~60%
+  - Female (sex=2): ~40%
+
+### Tissue Coverage
+
+54 GTEx tissues including:
+- **Blood**: Whole blood samples
+- **Brain**: Multiple brain regions (cortex, cerebellum, etc.)
+- **Heart**: Atrial and ventricular tissue
+- **Liver**: Hepatic tissue
+- **Muscle**: Skeletal muscle
+- **Adipose**: Subcutaneous and visceral fat
+- **Skin**: Sun-exposed and not sun-exposed
+- **And 47 more tissues**
+
+### Gene Coverage
+
+- **Total Genes**: 18,248 protein-coding genes
+- **Gene ID Type**: Ensembl gene IDs (via gene symbols)
+- **Expression Range**: 0 to ~1,000,000 TPM
+- **Median Genes Detected per Sample**: ~15,000 (TPM > 0)
+
+## Data Source & Citation
+
+### GTEx Project
+
+This dataset is derived from **ARCHS4** and the **GTEx (Genotype-Tissue Expression) Project**.
+
+### ARCHS4
+
+**ARCHS4** (All RNA-seq and ChIP-seq Sample and Signature Search) provides uniformly processed gene expression data from GEO and SRA.
+
+**ARCHS4 Citation**:
+```bibtex
+@article{lachmann2018massive,
+  title={Massive mining of publicly available RNA-seq data from human and mouse},
+  author={Lachmann, Alexander and Torre, Denis and Keenan, Alexandra B and Jagodnik, Kathleen M and Lee, Hoyjin J and Wang, Lily and Silverstein, Moshe C and Ma'ayan, Avi},
+  journal={Nature communications},
+  volume={9},
+  number={1},
+  pages={1366},
+  year={2018},
+  publisher={Nature Publishing Group}
+}
+```
+
+**ARCHS4 Portal**: https://maayanlab.cloud/archs4/
+
+**Data Version**: ARCHS4 v2.2+ (continuously updated)
+
+### GTEx
+
+The **GTEx Project** is supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.
+
+**GTEx Citation**:
+```bibtex
+@article{gtex2020,
+  title={The GTEx Consortium atlas of genetic regulatory effects across human tissues},
+  author={GTEx Consortium},
+  journal={Science},
+  volume={369},
+  number={6509},
+  pages={1318--1330},
+  year={2020},
+  publisher={American Association for the Advancement of Science}
+}
+```
+
+**GTEx Portal**: https://gtexportal.org/
+
+**Data Version**: GTEx Analysis Release v10 (2022-06-06)
+
+### aging-expressions Package
+
+This dataset was processed using the `aging-expressions` Python library:
+
+**GitHub**: https://github.com/longevity-genie/aging-expressions
+
+**Citation**:
+```bibtex
+@software{aging_expressions,
+  title={aging-expressions: Age-stratified gene expression analysis toolkit},
+  author={Longevity Genie Team},
+  year={2025},
+  url={https://github.com/longevity-genie/aging-expressions}
+}
+```
+
+### BulkFormer Model
+
+Gene filtering and compatibility are based on the **BulkFormer** foundation model:
+
+**BulkFormer Citation**:
+```bibtex
+@article{bulkformer2025,
+  title={BulkFormer: A large-scale foundation model for human bulk transcriptomes},
+  author={[Authors]},
+  journal={bioRxiv},
+  year={2025},
+  doi={10.1101/2025.06.11.659222}
+}
+```
+
+**GitHub**: https://github.com/your-org/BulkFormer
+
+## License
+
+This dataset is released under **CC BY 4.0 License** (Creative Commons Attribution 4.0 International).
+
+### Conditions
+
+- ✅ **Share**: Copy and redistribute the material in any medium or format
+- ✅ **Adapt**: Remix, transform, and build upon the material for any purpose
+- ✅ **Attribution**: You must give appropriate credit to both:
+  - The GTEx Project (original data source)
+  - The aging-expressions package (data processing)
+
+### GTEx Data Use Agreement
+
+Users of this dataset must also comply with the **GTEx Data Use Certification Agreement**:
+- Data is for research purposes only
+- Do not attempt to identify individual participants
+- Acknowledge GTEx in publications
+
+For full terms, see: https://gtexportal.org/home/dataUseAgreement
+
+## Limitations & Considerations
+
+### Age Limitations
+
+- **Age Brackets**: GTEx provides age as 10-year ranges, not exact ages
+- **Age Range**: Limited to 20-79 years (no samples < 20 or > 79)
+- **Estimated Age**: Midpoints are used (e.g., 54.5 for "50-59"), which introduces uncertainty
+
+### Sample Considerations
+
+- **Post-mortem Tissue**: All GTEx samples are from deceased donors
+- **Death Circumstances**: Variable (recorded in `death_time` column)
+- **Tissue Quality**: Quality varies by death circumstances and preservation
+- **Multiple Samples per Subject**: Same individual contributes multiple tissues (avoid subject leakage in train/test splits)
+
+### Gene Expression Limitations
+
+- **Gene Coverage**: 18,248 genes (not all human genes)
+- **Gene Filtering**: Limited to BulkFormer gene set
+- **TPM Normalization**: Assumes gene length and library size corrections are accurate
+- **Batch Effects**: Potential batch effects across collection sites and dates
+
+### Demographic Limitations
+
+- **Sex Only**: No gender identity information
+- **Limited Diversity**: GTEx v10 is primarily from donors of European ancestry
+- **No Disease Status**: Donors are generally healthy (post-mortem collection)
+
+## Ethical Considerations
+
+### Privacy
+
+- **De-identified Data**: All GTEx data is de-identified
+- **No Protected Health Information (PHI)**: Sample IDs are not linkable to individuals
+- **IRB Approved**: GTEx project has IRB approval from all participating sites
+
+### Bias & Fairness
+
+- **Demographic Bias**: Dataset skews toward European ancestry, male donors, and middle-aged individuals
+- **Tissue Availability Bias**: Some tissues are underrepresented due to collection feasibility
+- **Research Use Only**: Not suitable for clinical decision-making
+
+### Responsible Use
+
+- **Research Purposes**: This data is for research only, not clinical diagnostics
+- **Model Validation**: Models trained on this data should be validated on independent cohorts
+- **Transparency**: Report dataset characteristics when publishing results
+
+## Updates & Maintenance
+
+### Version History
+
+- **v1.0** (2025-01): Initial release with GTEx v10 data
+  - 9,662 samples, 18,248 genes
+  - TPM-normalized expression
+  - Age-stratified metadata
+
+### Future Plans
+
+- **ARCHS4 Integration**: Add ARCHS4 human and mouse samples
+- **DEE2 Metadata**: Incorporate SRA metadata for cross-dataset analysis
+- **Tissue Subsets**: Create tissue-specific subsets for faster loading
+- **Age Prediction Features**: Add BulkFormer-derived age predictions
+
+## Contact & Support
+
+### Questions & Issues
+
+- **GitHub Issues**: https://github.com/longevity-genie/aging-expressions/issues
+- **Discussions**: https://github.com/longevity-genie/aging-expressions/discussions
+
+### Contributing
+
+Contributions are welcome! See the [GitHub repository](https://github.com/longevity-genie/aging-expressions) for:
+- Bug reports
+- Feature requests
+- Documentation improvements
+- Additional analyses
+
+### Acknowledgments
+
+This dataset was created by the **Longevity Genie** team as part of our mission to accelerate aging research through open data and reproducible analysis.
+
+Special thanks to:
+- **GTEx Consortium** for making this invaluable resource available
+- **BulkFormer team** for the foundation model and gene annotations
+- **Open-source community** for tools like Polars, Pandas, and HuggingFace
+
+---
+
+**Last Updated**: January 2025  
+**Dataset Version**: 1.0  
+**Curator**: Longevity Genie Team
+

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