Multi-Omics Integration with Autoencoders and Graph Neural Networks

Open-source framework for extreme-scale multi-omics data integration

Overview

Open-source framework for multi-omics data integration using large-scale dual autoencoders (545M parameters) and graph neural networks. Demonstrates effective handling of ultra-high-dimensional biological data (132K genes × 7K metabolites) with extreme feature-to-sample ratios (6,619:1) in small-sample regimes (n=21).

Key Innovation: Privacy-preserving collaborative research with full biological data anonymization while maintaining scientific validity.

Problem Statement

Multi-omics integration faces critical challenges:

  • Ultra-high dimensionality: Genomic (100K+ features) and metabolomic (10K+ features) data vastly exceed sample sizes
  • Small sample regime: Typical clinical cohorts have n=20-100 samples due to cost constraints
  • Nonlinear relationships: Gene-metabolite associations involve complex regulatory cascades
  • Privacy concerns: Biological data requires careful anonymization for collaborative research

Traditional dimensionality reduction (PCA, t-SNE) assumes linearity; kernel methods don’t scale to 100K+ features.

Methodology

Data Characteristics

  • Transcriptomics: 132,129 genes (RNA-seq counts)
  • Metabolomics: 6,980 metabolites (abundance measurements)
  • Sample size: 21 samples (6,619:1 feature-to-sample ratio)
  • Privacy: Specific biological identities withheld; data fully anonymized

Preprocessing Pipeline

# Modality-specific normalization
genes_log2 = log2(gene_counts + 1)        # Handle zero counts
genes_zscore = (genes_log2 - mean) / std  # Z-score per gene

metabolites_zscore = (metabolites - mean) / std  # Z-score per metabolite

Dual Autoencoder Architecture

Gene Autoencoder (545M parameters):

  • Encoder: 132,129 → 8,192 → 1,024 → 128 → 64
  • Decoder: 64 → 128 → 1,024 → 8,192 → 132,129
  • Activation: ReLU Loss: MSE
  • Regularization: Dropout (0.3), Early Stopping (patience=10), 5-fold CV

Metabolite Autoencoder (30.8M parameters):

  • Encoder: 6,980 → 1,024 → 256 → 64
  • Decoder: 64 → 256 → 1,024 → 6,980
  • Activation: ReLU Loss: MSE
  • Regularization: Dropout (0.3), Early Stopping (patience=10), 5-fold CV

Training Strategy:

  • Independent training per modality to learn modality-specific representations
  • Cross-validation prevents overfitting in small-sample regime
  • Extensive dropout and early stopping for regularization

Association Discovery

Latent Space Correlation:

# Gene embeddings: (n_samples, 64)
# Metabolite embeddings: (n_samples, 64)

correlations = []
for gene_emb in gene_embeddings:
    for met_emb in metabolite_embeddings:
        rho, p_value = spearmanr(gene_emb, met_emb)
        correlations.append((gene_id, met_id, rho, p_value))

# FDR correction for multiple testing
adjusted_p = fdr_correction(p_values)
significant_pairs = filter(adjusted_p < 0.05)

Graph Neural Network Refinement (79.9K parameters):

  • Bipartite graph: Gene nodes ↔ Metabolite nodes
  • Edges: Significant correlations from latent space
  • GNN architecture: 2-layer Graph Convolutional Network
  • Purpose: Refine associations using graph topology

Results

Model Performance

Autoencoder Reconstruction:

  • Gene AE: Captures major transcriptomic variation despite 6,619:1 ratio
  • Metabolite AE: Successfully compresses metabolomic profiles
  • Latent representations show biologically meaningful structure

Association Discovery:

  • Identified gene-metabolite pairs with FDR-corrected significance
  • Bipartite GNN refines associations using graph structure
  • Biological validation: Known enzyme families confirmed in associations

Key Findings

  1. Large-scale autoencoders can handle extreme feature-to-sample ratios (6,619:1) through extensive regularization
  2. Latent space correlation provides interpretable association discovery
  3. GNN refinement improves biological plausibility of associations
  4. Privacy-preserving framework enables collaborative research without exposing sensitive biological identities

Privacy & Ethics

Data Anonymization Strategy

  • No biological identifiers: Sample names, patient IDs removed
  • Feature anonymization: Specific gene/metabolite names withheld in public repository
  • Aggregated reporting: Only statistical summaries and framework architecture shared
  • Collaborative approval: Data provider explicitly approved open-source release

Responsible Research Practices

  • Transparent limitation reporting (small sample size, specific biological context)
  • Framework generalizability emphasized over specific biological claims
  • Open-source code enables method verification without exposing private data

Technical Implementation

Framework Architecture

omics-ae-gnn/
├── data_preprocessing/        # Normalization pipelines
├── autoencoders/              # Dual AE architectures
│   ├── gene_ae.py            # 545M-parameter gene autoencoder
│   └── metabolite_ae.py      # 30.8M-parameter metabolite autoencoder
├── association_discovery/     # Correlation + FDR correction
├── gnn_refinement/           # Bipartite GNN (79.9K params)
└── visualization/            # t-SNE, UMAP, embedding analysis

Key Technologies

  • PyTorch (2.0+): Deep learning framework
  • PyTorch Geometric: Graph neural networks
  • scikit-learn: Preprocessing, cross-validation
  • scipy.stats: Spearman correlation, FDR correction
  • pandas/numpy: Data manipulation

Impact & Applications

Scientific Contributions

  1. Framework generalizability: Applicable to diverse multi-omics integration tasks
  2. Extreme-scale demonstration: Successful handling of 6,619:1 feature-to-sample ratio
  3. Privacy-preserving collaboration: Template for sharing methods without exposing data
  4. Open-source contribution: GitHub repository enables method reproduction and extension

Potential Applications

  • Precision medicine: Linking genomic profiles to metabolic phenotypes
  • Drug discovery: Identifying metabolic targets from transcriptomic signatures
  • Systems biology: Understanding gene-metabolite regulatory networks
  • Biomarker discovery: Multi-omics biomarker panels for disease stratification

Limitations & Future Work

Current Limitations

  • Small sample size (n=21): Limits generalizability; recommend n≥100 for production
  • Specific biological context: Associations may not generalize to other tissues/conditions
  • Computational cost: 545M-parameter models require GPU acceleration
  • Privacy-utility tradeoff: Anonymization prevents full biological interpretation in public forum

Future Directions

  1. Scaling to larger cohorts: Validate framework with n≥100 samples
  2. Transfer learning: Pre-train on public datasets (TCGA, GTEx) for few-shot adaptation
  3. Attention mechanisms: Relation-specific attention in GNN for interpretability
  4. Multi-modal extension: Integrate proteomics, epigenomics, clinical variables
  5. Federated learning: Enable multi-institution collaboration without data sharing

Code Availability

GitHub Repository: https://github.com/arnold117/omics-ae-gnn

Features:

  • Complete framework implementation (preprocessing → autoencoders → GNN)
  • Privacy-preserving design (no biological identities exposed)
  • Extensible architecture for diverse multi-omics tasks
  • Documentation and usage examples

Note: Specific biological data not included in repository to protect collaborator privacy. Framework designed for generalizability across datasets.

Acknowledgments

This work was conducted in collaboration with colleagues who generously approved open-source release of the analytical framework while protecting sensitive biological data. All identifiable information has been anonymized to enable methodological sharing without compromising privacy.

Project Status: Ongoing (December 2025 - Present)
Technologies: PyTorch, PyTorch Geometric, Python
Category: AI for Health, Computational Biology, Multi-Omics Integration