WITHDRAWN: Debiased network constrained sparse group lasso with applications to high-dimension longitudinal omics data

Music City Center 

There is a growing interest in longitudinal omics data paired with some longitudinal clinical outcome. Given a large set of continuous omics variables and some continuous clinical outcome, each measured for a few subjects at only a few time points, we seek to identify those variables that co-vary over time with the outcome in one or more treatment groups. To motivate this problem we study a dataset with hundreds of urinary metabolites along with Tuberculosis mycobacterial load as our clinical outcome, with the objective of identifying potential biomarkers for disease progression in two treatment groups. For such data clinicians usually apply simple linear mixed effects models which often lack power given the low number of replicates and time points. Our previous method, PROLONG, combines group lasso and network Laplacian penalties on first-differenced data, increasing power and utilizing the variance across both time and omics features. We extend this PROLONG model to multiple treatment groups by debiasing the group lasso + laplacian model and performing inference on the debiased estimator.

Omics

High Dimensional

Metabolomics

Regression 

Biometrics Section