ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets
This work addresses the problem of detecting complex phenotypes in biological datasets for researchers, but it is incremental as it builds on autoencoder-based methods with a revised loss function.
The paper tackles the challenge of identifying complex phenotypes in high-dimensional biological data by introducing ODBAE, a method that detects subtle and extreme outliers through latent relationships, and demonstrates its ability to identify knockout mice with multi-indicator phenotypes and reveal novel metabolism-related genes.
Identifying complex phenotypes from high-dimensional biological data is challenging due to the intricate interdependencies among different physiological indicators. Traditional approaches often focus on detecting outliers in single variables, overlooking the broader network of interactions that contribute to phenotype emergence. Here, we introduce ODBAE (Outlier Detection using Balanced Autoencoders), a machine learning method designed to uncover both subtle and extreme outliers by capturing latent relationships among multiple physiological parameters. ODBAE's revised loss function enhances its ability to detect two key types of outliers: influential points (IP), which disrupt latent correlations between dimensions, and high leverage points (HLP), which deviate from the norm but go undetected by traditional autoencoder-based methods. Using data from the International Mouse Phenotyping Consortium (IMPC), we show that ODBAE can identify knockout mice with complex, multi-indicator phenotypes - normal in individual traits, but abnormal when considered together. In addition, this method reveals novel metabolism-related genes and uncovers coordinated abnormalities across metabolic indicators. Our results highlight the utility of ODBAE in detecting joint abnormalities and advancing our understanding of homeostatic perturbations in biological systems.