This project demonstrates how to integrate multi-omics data using a Variational Autoencoder (VAE) with a classification component. The process includes dimensionality reduction and feature importance analysis using Principal Component Analysis (PCA) and SHAP (SHapley Additive exPlanations).
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Data Generation and Preprocessing:
- Synthetic Data Generation: Creates synthetic multi-omics datasets (scRNA-seq, scATAC-seq, cell painting).
- Data Combination: Merges the different data types into a single dataset.
- Data Normalization: Normalizes the combined dataset to ensure consistency.
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VAE Model:
- Architecture: A VAE model with an encoder, decoder, and classifier is defined. The encoder learns latent representations, the decoder reconstructs the input data, and the classifier performs binary classification.
- Custom Loss Function: The model is trained using a custom loss function that combines reconstruction loss, KL divergence, and classification loss.
- Training: The model is trained using a custom training loop to optimize the loss function.
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Latent Representation and Classification:
- Latent Representations: Obtains latent representations from the trained encoder.
- Classification: Performs binary classification on the latent space representations and evaluates accuracy.
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Dimensionality Reduction and Feature Importance Analysis:
- PCA: Apply PCA for dimensionality reduction and use the elbow method to determine the optimal number of principal components.
- SHAP Analysis:
- Model Training: Train a Random Forest model on the top PCA components to predict a subset of original features.
- SHAP Values: Calculate SHAP values to explain the impact of each PCA component on the model’s predictions.
- Visualization: Generate SHAP summary and dependence plots to visualize feature importance.
- Python (>=3.6)
- NumPy
- TensorFlow
- Keras
- Matplotlib
- Scikit-learn
- SHAP
- Install the Required Libraries: Ensure all necessary libraries installed.
- Run the Provided Python Code: Execute the Python code in an environment such as Google Colab or a local setup.
- Adjust Parameters: Modify parameters such as the number of epochs, batch size, and latent dimension as needed for your specific use case.
- Synthetic Data: The provided code uses some fake simplified synthetic data. Replace it with real multi-omics preprocessed (ready-to-encode) data for practical applications.
- Data Sparsity: When using real single-cell data, consider regularizing sparsity (e.g., using dropout) and exploring alternative architectures such as Convolutional Neural Networks (CNNs) for imaging data.
- Model Exploration: Experiment with different VAE architectures and hyperparameters for optimal performance (e.g., using different loss functions).
- SHAP Analysis: SHAP can be applied to different subsets of features or machine learning models to enhance interpretability and explainable AI.