longevity-db/pan-cancer-nuclei-seg

import pandas as pd
from sklearn.ensemble import RandomForestRegressor

# Load data
nuclei_df = pd.read_parquet('nuclei_features.parquet')
clinical_df = pd.read_parquet('clinical_metadata.parquet')

# Merge with age data
data = nuclei_df.merge(clinical_df, on='tcga_barcode')

# Train age prediction model
features = ['area', 'aspect_ratio', 'circularity', 'convexity']
X = data[features]
y = data['age_at_diagnosis']

model = RandomForestRegressor(n_estimators=100)
model.fit(X, y)

# Feature importance for aging
aging_features = pd.DataFrame({
    'feature': features,
    'importance': model.feature_importances_
}).sort_values('importance', ascending=False)

# Compare nuclear features across age groups
young = data[data['age_at_diagnosis'] < 50]
old = data[data['age_at_diagnosis'] > 70]

senescence_markers = {}
for feature in features:
    young_mean = young[feature].mean()
    old_mean = old[feature].mean()
    fold_change = old_mean / young_mean
    senescence_markers[feature] = {
        'young_mean': young_mean,
        'old_mean': old_mean,
        'fold_change': fold_change
    }

# Create age bins
data['age_group'] = pd.cut(data['age_at_diagnosis'], 
                          bins=[20, 40, 55, 70, 90], 
                          labels=['Young', 'Middle', 'Mature', 'Elderly'])

# Age-related trends
age_trends = data.groupby('age_group')[features].agg(['mean', 'std'])

@dataset{nuclear_aging_morphometry_2025,
  title={Nuclear Aging Morphometry Dataset: TCGA Nuclei Features for Longevity Research},
  author={[Your Name]},
  year={2025},
  publisher={HuggingFace Hub},
  url={https://huggingface.co/datasets/longevity-db/nuclear-aging-morphometry}
}

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the dataset
nuclei_df = pd.read_parquet('nuclei_features.parquet')
clinical_df = pd.read_parquet('clinical_metadata.parquet')

# Merge datasets
data = nuclei_df.merge(clinical_df, on='tcga_barcode')

# Basic aging analysis
plt.figure(figsize=(12, 8))

# Plot 1: Area vs Age
plt.subplot(2, 2, 1)
plt.scatter(data['age_at_diagnosis'], data['area'], alpha=0.1)
plt.xlabel('Age at Diagnosis')
plt.ylabel('Nuclear Area')
plt.title('Nuclear Area vs Age')

# Plot 2: Aspect Ratio vs Age  
plt.subplot(2, 2, 2)
plt.scatter(data['age_at_diagnosis'], data['aspect_ratio'], alpha=0.1)
plt.xlabel('Age at Diagnosis')
plt.ylabel('Aspect Ratio')
plt.title('Nuclear Elongation vs Age')

# Plot 3: Age distribution
plt.subplot(2, 2, 3)
plt.hist(data['age_at_diagnosis'], bins=20, edgecolor='black')
plt.xlabel('Age at Diagnosis')
plt.ylabel('Number of Nuclei')
plt.title('Age Distribution')

# Plot 4: Feature correlation with age
plt.subplot(2, 2, 4)
age_corr = data[['age_at_diagnosis', 'area', 'aspect_ratio', 'circularity', 'convexity']].corr()['age_at_diagnosis'].drop('age_at_diagnosis')
age_corr.plot(kind='bar')
plt.title('Feature Correlation with Age')
plt.xticks(rotation=45)

plt.tight_layout()
plt.show()

# Age prediction using nuclear morphology
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

# Prepare features
features = ['area', 'aspect_ratio', 'circularity', 'convexity', 'perimeter']
X = data[features]
y = data['age_at_diagnosis']

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)
print(f"Mean Absolute Error: {mae:.2f} years")

### Advanced Analysis with Technical Parameters

## 📊 Dataset Statistics

- **Total Nuclei**: ~1,000,000+ individual nuclei
- **Patients**: ~400 unique TCGA patients  
- **Age Range**: 20-90 years
- **Cancer Type**: Bladder Urothelial Carcinoma (BLCA)
- **Image Modality**: H&E histopathology
- **Magnification**: 20x (0.5 μm/pixel)
- **Success Rate**: ~89% successful feature extractions
- **Technical Completeness**: 100% metadata coverage for all parameters

### **Quality Metrics**
- **Segmentation Quality**: Validated by `levelset_num_iters` convergence
- **Size Filtering**: `min_size`/`max_size` removes 5-10% of artifacts
- **Spatial Coverage**: Full WSI sampling via systematic tiling
- **Physical Validation**: `PhysicalSize` matches calculated areas (r² > 0.99)

## 🔬 Research Opportunities

This dataset enables research in:

- **Digital Pathology Aging Clocks**: Nuclear morphology-based age prediction
- **Senescence Detection**: ML models for identifying senescent cells
- **Cancer-Aging Interactions**: How aging affects cancer morphology
- **Biomarker Discovery**: Novel aging biomarkers from nuclear shape
- **Drug Screening**: Morphometric assessment of senolytic compounds
- **Spatial Aging Analysis**: Regional variation in nuclear aging patterns
- **Multi-scale Studies**: Tile/patch-level aging heterogeneity

### **Reproducibility & Technical Validation**

The comprehensive technical metadata enables:

1. **Algorithm Comparison**: Test different segmentation parameters
2. **Quality Assessment**: Filter by processing metrics (`levelset_num_iters`, etc.)
3. **Scale Validation**: Verify physical measurements using `mpp` values
4. **Spatial Analysis**: Use tile coordinates for location-based studies
5. **Pipeline Reproduction**: Exact parameter sets for replication

## ⚖️ License and Usage

- **License**: CC BY 4.0 (Creative Commons Attribution)
- **Data Source**: TCGA (public domain)
- **Usage**: Research and educational purposes
- **Attribution**: Please cite this dataset and original TCGA publications

## 🤝 Contributing

This dataset was created for the **Longevity x AI Hackathon 2025**. Contributions, improvements, and additional analyses are welcome!

For questions or collaboration opportunities, please contact [etai.sapoznik@gmail.com] & [srinulade1@gmail.com].

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*This dataset represents a novel intersection of cancer genomics, digital pathology, and aging research, enabling unprecedented insights into the cellular basis of human aging.*
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license: cc
language:
- en
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