BRCA Multi-omics GNN Notebook

Overview

This Jupyter Notebook implements a graph neural network (GNN) pipeline for binary survival classification (alive vs. deceased) using multi-omics breast cancer data. All components,from data loading through visualization, training, evaluation, and interpretability, are structured as executable cells, ideal for interactive exploration.

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Table of Contents

1. Quick Start
2. Prerequisites
3. Notebook Structure & Sections
4. How to Run
5. Key Notebook Cells
6. Customization Tips
7. Saving Outputs
8. Troubleshooting & FAQs
9. Next Steps & Enhancements
10. Contact & License

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1. Quick Start

1. Open the notebook (e.g., BRCA_TCGA.ipynb) in Jupyter (Notebook or Lab).
2. Ensure data.csv is placed in the same directory.
3. Execute cells sequentially from top to bottom.
4. Visualizations and progress will appear inline as you go.

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2. Prerequisites

Make sure the following packages are installed (via pip or your preferred manager):

pip install numpy pandas matplotlib seaborn scikit-learn tensorflow networkx scipy

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3. Notebook Structure & Sections

The notebook is organized into these modular sections:

• Imports & Setup: loads libraries, configures warning filters.
• Data Loading & Preprocessing: includes load_and_preprocess_data() cell with detailed printouts.
• Adjacency (Graph) Construction: create_adjacency_matrix() with optional threshold/KNN logic and memory-reducing feature selection.
• Visualization: inline plots for class distribution, feature types, adjacency heatmap, PCA, etc.
• Model Definition: code cell defining GraphNeuralLayer and build_gnn_model(), followed by .summary().
• Train & Evaluate: cells to train the model with callbacks and to evaluate with accuracy, ROC, classification report.
• Results Plots: training curves, confusion matrix, ROC curves displayed inline.
• Feature Importance: gradient-based attribution visualization for top features.
• Results Summary: prints top-10 features with importance at the bottom.

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4. How to Run

• Open the notebook in Jupyter and run each cell in order.
• For long-running cells (like training), you’ll see progress bars from TensorFlow inline.
• If interrupted, you can skip back to the last completed step (e.g., reload model or re-run training cell).

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5. Key Notebook Cells (Highlights)

Section	Description
Data Preprocessing	Handles missing values, label cleaning, upsampling, and prints distribution dynamics.
Adjacency Construction	Efficiently builds a graph structure from correlations or neighbor graphs; includes feature reduction.
Model & Training	Defines custom GNN layer, composes the model, shows architecture summary, and fits with early stopping and checkpointing.
Evaluation & Visualization	Generates performance metrics and decision plots directly in notebook for easy analysis.
Feature Interpretation	Uses gradient-based attribution to highlight influential features, visualized as a bar chart.

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6. Customization Tips

• Adjust graph method: Toggle between correlation vs. k-NN, or change the threshold parameter inline.
• Tweak model architecture: Edit layer sizes, add/drop layers, or change dropout rates in the model definition cell.
• Balance strategies: Replace random upsampling with SMOTE by adding a cell (via imbalanced-learn).
• Use sparse adjacency: You can modify adjacency construction cells to use sparse matrices if needed for scalability.
• Try interpretability alternatives: Add a cell to incorporate Integrated Gradients (TensorFlow example) for more robust attributions.

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7. Saving Outputs

• Visualizations show inline automatically.

• To save figures, add:

  fig.savefig('outputs/figure_name.png', dpi=300)

• The model checkpoint (bestmodel.h5) is saved automatically in training cells.

• For later predictions, persist the scaler object using pickle.

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8. Troubleshooting & FAQs

• Out-of-memory crashes: Reduce max_features in adjacency cell or lower the batch size.

• Missing data.csv error: Ensure the file is in the same folder or update path in the first cell.

• Graphs or plots not appearing: Confirm matplotlib inline mode is enabled (%matplotlib inline) at top.

• Random seed consistency: If you need reproducible training, add seed setting cells:

  import numpy as np, tensorflow as tf, random
  np.random.seed(42)
  tf.random.set_seed(42)
  random.seed(42)

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9. Next Steps & Enhancements

• Convert to script: Extract logic into .py modules with an entry-point like run.py or use nbconvert for scripting.
• Advanced imbalanced handling: Try SMOTE, class weighting, or focal loss alternatives.
• Incorporate biological networks: Use STRING, BioGRID, or curated gene-gene interaction priors to replace or augment the correlation graph.
• Explore model variants: Edge-conditioned GNNs, GAT (attention), or multi-layer/multi-omics heterogeneous graphs.

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10. Contact & License

• License: GPL 3.
• Questions / improvements: Feel free to file a GitHub issue or PR.