EpiBench Analysis: Detailed Process Guide

A comprehensive guide to the analysis process with EpiBench. Last updated: 2024-11-22

Analysis Overview

EpiBench provides a systematic approach to epigenetic data analysis. This guide outlines each step in detail, from data preparation to result interpretation.

Stage 1: Data Processing and Preparation

Genomic Sequence Processing

# Load genomic sequences
from epibench.data import SequenceLoader
 
sequence_loader = SequenceLoader(
    reference_genome="path/to/genome.fa",
    context_size=1000
)
 
sequences = sequence_loader.load_from_bed("regions.bed")

Epigenetic Data Integration

# Load histone data
from epibench.data import HistoneLoader
 
histone_loader = HistoneLoader()
histone_data = histone_loader.load_multiple_marks({
    "H3K4me3": "h3k4me3.bw",
    "H3K27ac": "h3k27ac.bw"
})
 
# Data integration
from epibench.data import DataIntegrator
integrator = DataIntegrator()
integrated_data = integrator.combine(
    sequences=sequences,
    histone_data=histone_data
)

Stage 2: Feature Engineering

# Extract sequence features
from epibench.features import SequenceFeatureExtractor
 
extractor = SequenceFeatureExtractor()
sequence_features = extractor.one_hot_encode(sequences)
 
# Process histone features
from epibench.features import HistoneProcessor
 
processor = HistoneProcessor()
histone_features = processor.normalize(histone_data)

Stage 3: Model Construction and Training

Model Architecture

# Define neural network model
from epibench.models import MultibranchCNN
 
model = MultibranchCNN(
    sequence_length=1000,
    n_histone_marks=2,
    kernel_sizes=[3, 9, 27],
    dropout_rate=0.3
)
 
# Configure training
model.configure_training(
    learning_rate=0.001,
    batch_size=64,
    n_epochs=100
)

Training Process

# Split data
train_data, val_data, test_data = integrator.train_val_test_split(
    integrated_data,
    train_ratio=0.7,
    val_ratio=0.15,
    test_ratio=0.15
)
 
# Train model
history = model.train(
    train_data=train_data,
    val_data=val_data,
    save_best=True
)

Stage 4: Evaluation and Interpretation

Performance Assessment

# Evaluate model
metrics = model.evaluate(test_data)
print(f"Test accuracy: {metrics['accuracy']:.4f}")
print(f"Test AUC: {metrics['auc']:.4f}")
 
# Generate predictions
predictions = model.predict(test_data)

Model Interpretation

# Interpret predictions
from epibench.interpret import IntegratedGradients
 
explainer = IntegratedGradients(model)
attributions = explainer.explain(test_data[0])
 
# Visualize results
from epibench.visualization import AttributionPlot
 
plotter = AttributionPlot()
plotter.sequence_attribution(
    sequence=test_data[0].sequence,
    attributions=attributions.sequence
)

Stage 5: Result Reporting

# Generate comprehensive report
from epibench.reporting import HTMLReport
 
report = HTMLReport(
    title="Epigenetic Analysis Report",
    author="Your Name"
)
 
report.add_metrics(metrics)
report.add_figures([
    "attribution_plot.png",
    "performance_metrics.png"
])
 
report.generate("analysis_report.html")

Alternative Analysis Approaches

Classification Analysis

For classification instead of regression:

# Configure for classification
model = MultibranchCNN(
    sequence_length=1000,
    n_histone_marks=2,
    task="classification",
    n_classes=2
)

Transfer Learning

To leverage pre-trained models:

# Load pre-trained model
from epibench.models import load_model
 
pretrained = load_model("pretrained_model.pt")
pretrained.freeze_feature_layers()
 
# Fine-tune
pretrained.train(
    train_data=new_data,
    learning_rate=0.0001,  # Lower learning rate for fine-tuning
    n_epochs=20
)

Best Practices

1. Data Quality

   • Verify genomic coordinate consistency
   • Check for batch effects in histone data
   • Normalize signals appropriately

2. Model Selection

   • Start with simpler models as baselines
   • Adjust architecture based on data complexity
   • Use cross-validation for hyperparameter tuning

3. Results Interpretation

   • Compare with known biological mechanisms
   • Validate patterns across multiple samples
   • Consider the biological context of predictions

4. Computational Efficiency

   • Use appropriate batch sizes
   • Leverage GPU acceleration when available
   • Cache intermediate results for large datasets

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For technical setup details, see the Technical Requirements page. For a practical example, check the AML Case Study.