High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-dimensional genomics studies are susceptible to unmeasured biological processes that obscure disease-specific signals.
• Current methods can estimate latent confounders but lack the ability to quantify the robustness of discoveries to hypothetical confounding.

Purpose of the Study:

• To introduce sensGAN, a deep-learning adversarial framework for quantitative sensitivity analysis in genomics.
• To systematically explore the confounding spectrum and identify confounder-sensitive genes.

Main Methods:

• Developed sensGAN, a deep-learning adversarial framework.
• Learned "worst-case" latent variables to nullify gene associations under predictive-gain constraints.
• Identified minimum confounding strength required to explain observed effects.

Main Results:

• sensGAN accurately recovers latent structures in diverse simulations.
• Outperformed existing methods in identifying confounder-sensitive genes.
• Applied to Alzheimer's disease microglia, it prioritized robust disease pathways and isolated signals from co-occurring pathologies.

Conclusions:

• sensGAN enables a formal, quantitative sensitivity analysis for high-dimensional genomics studies.
• The framework effectively distinguishes true disease signals from confounding effects.
• Provides a robust approach for prioritizing reliable findings in complex biological data.