60: TPClust: Temporal Profile-Guided Disease Subtyping Using High-Dimensional Omics Data

Music City Center 

Disease subtyping using unsupervised clustering of omics data often results in subtypes with limited clinical relevance, while existing supervised methods are not suitable for longitudinal data. To address this, we developed a novel latent generative model for disease subtyping that integrates longitudinal clinical data and high-dimensional omics data. Our method comprises two components: a multinomial logistic regression using omics to define subtypes and a longitudinal association model capturing time-varying relationships between clinical variables. These are integrated via a mixture regression. We include omics feature selection and smooth estimation of time-varying associations into the model fitting. A multiplier bootstrap was used to construct confidence intervals for time-varying effects. We validated our method through simulations and applied it to 1,020 adults from the Religious Orders Study and Memory and Aging Project (ROS/MAP)-two longitudinal cohorts for investigating Alzheimer's Disease (AD). Our approach captures the time-varying effects of AD risk factors and enables accurate inference on these effects, leading to the detection of clinically meaningful subtypes.

Disease subtyping

Machine learning

Semi-parametric model

High-dimensional omics

Longitudinal data

Supervised clustering 

Section on Statistics in Genomics and Genetics