A hybrid model combining diffusion and white-box transformers for transparent scRNA-seq generation
Transparent and efficient synthetic biology data generation using hybrid transformer architecture
https://arxiv.org/abs/2411.06785
🎯 Original Problem:
Single-cell RNA sequencing (scRNA-seq) data acquisition faces high costs and limited sample availability. Traditional generative models like GANs and VAEs struggle with instability and mode collapse when generating synthetic scRNA-seq data.
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🔧 Solution in this Paper:
→ Introduces White-Box Diffusion Transformer - a hybrid model combining Diffusion model with White-Box transformer for generating synthetic scRNA-seq data
→ Uses Multi-Head Subspace Self-Attention (MSSA) for data compression instead of standard attention
→ Implements Iterative Shrinkage Thresholding Algorithm (ISTA) for sparsification replacing feed-forward networks
→ Integrates mathematically interpretable White-Box components with Diffusion process for complete transparency
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💡 Key Insights:
→ White-Box components provide mathematical interpretability while maintaining generation quality
→ MSSA reduces coding rate through gradient descent in multiple subspaces
→ ISTA achieves sparsity through iterative optimization with ReLU activation
→ Hybrid architecture balances generation quality with interpretability
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📊 Results:
→ 50% faster training time per epoch compared to standard Diffusion Transformer
→ Similar or better performance metrics (KL divergence, Wasserstein distance, MMD)
→ Successfully generated 5x larger synthetic datasets while maintaining quality
→ Demonstrated robustness across 6 different scRNA-seq datasets
