Longitudinal Bayes Omics Regression Discovery

Music City Center 

Clinicians are increasingly interested in discovering computational biomarkers from short-term longitudinal omics data sets. This work focuses on Bayesian regression and variable selection for longitudinal omics datasets, which can quantify uncertainty and control false discovery. In both approaches, we use the first difference scale of longitudinal predictor and the response. In our univariate approach, Zellner's g prior is used with two different options of the tuning parameter g: g=sqrt n and a g that minimizes Stein's unbiased risk estimate (SURE). Bayes Factors were used to quantify uncertainty and control for false discovery. In the multivariate approach, we use Bayesian Group LASSO with a spike and slab prior for group variable selection. We compare our method against commonly used linear mixed effect models on simulated data and real data from a Pulmonary Tuberculosis study on metabolite biomarker selection. With an automated selection of hyperparameters, the Zellner's g prior and Multivariate Bayesian Group Lasso spike and slab approach correctly identifies target metabolites with high specificity and sensitivity across various simulation and real data scenarios.

Disease Progression

Feature Selection

Mixed Models

Bayesian Group Lasso

Uncertainty Quantification

Zellners g-prior 

Section on Bayesian Statistical Science