RALPS

Regularized Adversarial Learning Preserving Similarity (RALPS): Multi-batch normalization for untargeted metabolomics

Regularized Adversarial Learning Preserving Similarity (RALPS) normalizes multi-batch untargeted metabolomics data generated by high-resolution mass spectrometry by mitigating batch effects while preserving biological identities, spectral properties, and coefficients of variation.

Key Features:

• Adversarial Batch Correction: Applies deep adversarial learning with a three-term loss function to reduce inter-batch differences.
• Biological Signal Preservation: Maintains essential biological identities, spectral characteristics, and coefficients of variation during normalization.
• Regularization Strategy: Incorporates regularization to preserve biological signals under varying experimental conditions.
• Missing Value Handling: Processes incomplete metabolomics datasets without compromising normalization performance.
• Scalability: Maintains robustness across datasets of varying sizes and complexities.

Scientific Applications:

• Untargeted Metabolomics: Enables comparative analysis of complex biological, clinical, or environmental samples across multiple experimental batches.
• Multi-batch Study Integration: Supports harmonization of large-scale metabolomics datasets for cross-experiment analysis.

Methodology:

RALPS employs deep adversarial learning with a three-term loss function to model and remove batch-specific variation in untargeted metabolomics mass spectrometry data. A regularization component constrains the model to preserve biological similarity, spectral properties, and coefficients of variation while correcting batch effects across heterogeneous experimental designs.